This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
This document discusses using a genetic algorithm to solve the travelling salesman problem. It begins with an introduction to the travelling salesman problem as an NP-complete problem to find the shortest route visiting each city once. It then provides an overview of genetic algorithms and their use of evolutionary concepts like selection of the fittest to find approximate solutions. The document outlines the genetic algorithm process including encoding routes as chromosomes, calculating fitness, selecting parents for crossover and mutation to create new offspring, and repeating until an optimal solution is found. It provides details of the genetic algorithm implementation for the travelling salesman problem.
Solving the traveling salesman problem by genetic algorithmAlex Bidanets
The document discusses the traveling salesman problem and genetic algorithms. The traveling salesman problem involves finding the shortest route to visit each city on a list only once and return to the origin city. Genetic algorithms provide a method to solve optimization problems like the traveling salesman problem. Genetic algorithms work by initializing a population of solutions and using operators like crossover and mutation to generate new populations, selecting the fittest solutions to reproduce until a condition is met. The genetic algorithm approach allows the traveling salesman problem to be solved effectively without prior knowledge of the problem.
The document provides an overview of genetic algorithms, including their inspiration from evolution, the basic algorithm, why they work, strengths and weaknesses, and applications. It summarizes the encoding, selection, crossover, and mutation steps of the basic genetic algorithm. It also gives examples of genetic algorithms applied to the traveling salesman problem (TSP), including encoding solutions and crossover/mutation operators.
advance operators.
explain about the diploid , dominance, and partial match crossover and the order crossover
Technologies
Multi objective optimization , knowledge base technologies hibrid, parallel computing
Performance of genetic algorithm is flexible enough to make it applicable to a wide range of problems, such as the problem of placing N queens on N by N chessboard in order that no two queens can attack each other which is known as ‘n-Queens problem.
Lack of information about details of the problem made genetic algorithm confused in searching state space of the problem
The document discusses using a genetic algorithm to optimize the mass design of a single-stage helical gear unit. The objective is to minimize the total mass of the gear unit, which is calculated based on the volumes and densities of its various components. The design must satisfy 38 constraints related to gear ratios, stresses, clearances, manufacturability, and component life. A genetic algorithm is applied to search for the design variable values that minimize mass subject to all constraints.
The document provides an overview of genetic algorithms, including their history, principles, components, and applications. Specifically, it discusses how genetic algorithms can be used to solve the traveling salesman problem (TSP) through permutation encoding of cities, calculating fitness based on total tour distance, and using techniques like order-1 crossover to preserve city order in offspring.
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
This document discusses using a genetic algorithm to solve the travelling salesman problem. It begins with an introduction to the travelling salesman problem as an NP-complete problem to find the shortest route visiting each city once. It then provides an overview of genetic algorithms and their use of evolutionary concepts like selection of the fittest to find approximate solutions. The document outlines the genetic algorithm process including encoding routes as chromosomes, calculating fitness, selecting parents for crossover and mutation to create new offspring, and repeating until an optimal solution is found. It provides details of the genetic algorithm implementation for the travelling salesman problem.
Solving the traveling salesman problem by genetic algorithmAlex Bidanets
The document discusses the traveling salesman problem and genetic algorithms. The traveling salesman problem involves finding the shortest route to visit each city on a list only once and return to the origin city. Genetic algorithms provide a method to solve optimization problems like the traveling salesman problem. Genetic algorithms work by initializing a population of solutions and using operators like crossover and mutation to generate new populations, selecting the fittest solutions to reproduce until a condition is met. The genetic algorithm approach allows the traveling salesman problem to be solved effectively without prior knowledge of the problem.
The document provides an overview of genetic algorithms, including their inspiration from evolution, the basic algorithm, why they work, strengths and weaknesses, and applications. It summarizes the encoding, selection, crossover, and mutation steps of the basic genetic algorithm. It also gives examples of genetic algorithms applied to the traveling salesman problem (TSP), including encoding solutions and crossover/mutation operators.
advance operators.
explain about the diploid , dominance, and partial match crossover and the order crossover
Technologies
Multi objective optimization , knowledge base technologies hibrid, parallel computing
Performance of genetic algorithm is flexible enough to make it applicable to a wide range of problems, such as the problem of placing N queens on N by N chessboard in order that no two queens can attack each other which is known as ‘n-Queens problem.
Lack of information about details of the problem made genetic algorithm confused in searching state space of the problem
The document discusses using a genetic algorithm to optimize the mass design of a single-stage helical gear unit. The objective is to minimize the total mass of the gear unit, which is calculated based on the volumes and densities of its various components. The design must satisfy 38 constraints related to gear ratios, stresses, clearances, manufacturability, and component life. A genetic algorithm is applied to search for the design variable values that minimize mass subject to all constraints.
The document provides an overview of genetic algorithms, including their history, principles, components, and applications. Specifically, it discusses how genetic algorithms can be used to solve the traveling salesman problem (TSP) through permutation encoding of cities, calculating fitness based on total tour distance, and using techniques like order-1 crossover to preserve city order in offspring.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
The document describes genetic algorithms, which are inspired by biological evolution. It discusses how genetic algorithms work by starting with a random population that undergoes selection, crossover, and mutation to generate new solutions. The population evolves over multiple generations as higher-fitness solutions are more likely to be selected for reproduction and combination with other solutions. This evolutionary process can help search large problem spaces to find optimal or near-optimal solutions.
This document discusses genetic algorithms and their use for optimization problems. It begins by defining genetic algorithms as search and optimization techniques based on Darwin's principle of natural selection. It then outlines the components and working of genetic algorithms, including encoding potential solutions as chromosomes, selecting chromosomes based on their fitness, and generating new solutions through crossover and mutation of parents. The document provides an example problem of using genetic algorithms to generate mathematical expressions that equal a target value.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
1. Genetic algorithms are a class of probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance.
2. They maintain a population of candidate solutions and make the population evolve iteratively by applying operators like selection, crossover and mutation.
3. Genetic algorithms are well-suited for hard optimization problems where little is known about the search space.
Genetic programming is an evolutionary computation technique that can automatically solve problems without requiring the user to specify the form of the solution in advance. It works by generating an initial population of computer programs randomly, then uses genetic operations like crossover and mutation to breed new programs, evaluating their fitness at each generation. The fittest programs survive and produce offspring for the next generation. Programs in GP are represented as syntax trees with functions as internal nodes and variables/constants as leaves. GP has been successfully applied to problems like circuit design, predictive modeling, and control systems optimization.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This document provides an overview of genetic algorithms (GAs). It describes Holland's simple genetic algorithm (SGA) model including representation, selection, crossover and mutation operators. Real-valued and permutation representations are discussed along with associated operators. Alternative population models and selection mechanisms are also summarized.
This document provides an introduction to genetic algorithms. It begins by discussing the origins of genetic algorithms, which were inspired by Darwin's theory of evolution. It then explains the basic concepts and terminology used in genetic algorithms, such as populations, chromosomes, fitness functions, selection, crossover, and mutation. The document provides pseudocode for how genetic algorithms work and walks through a numerical example to optimize the equation a + 2b + 3c + 4d = 30 using a genetic algorithm. Key steps explained include initialization, evaluation, selection, crossover, and mutation over multiple generations to arrive at a solution.
Genetic programming is an optimization technique inspired by biological evolution that uses techniques like mutation, crossover, fitness functions and multiple generations to evolve programs that can solve complex problems. It works by initializing a random population of computer programs and then applying genetic operations to the fittest programs each generation to evolve increasingly better solutions over time without needing human guidance. Common applications of genetic programming include automatic programming, symbolic regression, data modeling, engineering design, game strategies and more.
Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document discusses software module clustering using genetic algorithms and hill climbing techniques. It introduces genetic algorithms and hill climbing algorithms and how they can be applied to software module clustering. Specifically, it proposes using multiple hill climbs first to gather information about the search landscape, which is then used to define "building blocks" to improve subsequent searches done by genetic algorithms. The results of empirical studies using this novel approach show it to be effective at software module clustering.
Toward a Natural Genetic / Evolutionary Algorithm for Multiobjective Optimiza...Startup
New Genetic or Evolutionary Algorithm for multiobjective optimization, that attempts to find tradeoff solutions and scales easily with increase in parameter space as well as objective space. Does not use complex niche calculation that is used in existing multiobjective genetic algorithms.
This document summarizes a research paper that proposes using a genetic algorithm to solve the travelling salesman problem (TSP). It begins by defining the TSP and explaining that it is NP-hard. The document then reviews various existing approaches that have used genetic algorithms and other metaheuristics to solve TSP. It proposes a genetic algorithm with tournament selection, two-point crossover, and interchange mutation operators. The algorithm is tested on sample problems with 15 cities and is shown to find optimal or near-optimal solutions. In conclusion, the document argues that genetic algorithms can efficiently find good solutions to TSP, especially when combined with knowledge from heuristic methods.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research.
The document discusses the traveling salesman problem (TSP) which aims to find the shortest route for a salesman to visit each city once and return to the starting city. While computers can solve TSP problems, the time required increases enormously with the number of cities - solving a 15,000 city problem took over 20 years, and an 85,000 city problem would take over 130 computer years. Therefore, exact solutions are only efficient for small problems, as increasing the problem size significantly lengthens the computation time required.
The document discusses the Travelling Salesman Problem (TSP), which aims to find the shortest route to visit each city in a list exactly once and return to the origin city. It describes TSP as an NP-hard problem, belonging to the complexity class NP-complete. The document provides background on TSP, explaining it cannot be solved in polynomial time using techniques like linear programming. While an efficient solution to the general TSP has not been found, there are approximation algorithms that provide near-optimal solutions.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
The document describes genetic algorithms, which are inspired by biological evolution. It discusses how genetic algorithms work by starting with a random population that undergoes selection, crossover, and mutation to generate new solutions. The population evolves over multiple generations as higher-fitness solutions are more likely to be selected for reproduction and combination with other solutions. This evolutionary process can help search large problem spaces to find optimal or near-optimal solutions.
This document discusses genetic algorithms and their use for optimization problems. It begins by defining genetic algorithms as search and optimization techniques based on Darwin's principle of natural selection. It then outlines the components and working of genetic algorithms, including encoding potential solutions as chromosomes, selecting chromosomes based on their fitness, and generating new solutions through crossover and mutation of parents. The document provides an example problem of using genetic algorithms to generate mathematical expressions that equal a target value.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
1. Genetic algorithms are a class of probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance.
2. They maintain a population of candidate solutions and make the population evolve iteratively by applying operators like selection, crossover and mutation.
3. Genetic algorithms are well-suited for hard optimization problems where little is known about the search space.
Genetic programming is an evolutionary computation technique that can automatically solve problems without requiring the user to specify the form of the solution in advance. It works by generating an initial population of computer programs randomly, then uses genetic operations like crossover and mutation to breed new programs, evaluating their fitness at each generation. The fittest programs survive and produce offspring for the next generation. Programs in GP are represented as syntax trees with functions as internal nodes and variables/constants as leaves. GP has been successfully applied to problems like circuit design, predictive modeling, and control systems optimization.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This document provides an overview of genetic algorithms (GAs). It describes Holland's simple genetic algorithm (SGA) model including representation, selection, crossover and mutation operators. Real-valued and permutation representations are discussed along with associated operators. Alternative population models and selection mechanisms are also summarized.
This document provides an introduction to genetic algorithms. It begins by discussing the origins of genetic algorithms, which were inspired by Darwin's theory of evolution. It then explains the basic concepts and terminology used in genetic algorithms, such as populations, chromosomes, fitness functions, selection, crossover, and mutation. The document provides pseudocode for how genetic algorithms work and walks through a numerical example to optimize the equation a + 2b + 3c + 4d = 30 using a genetic algorithm. Key steps explained include initialization, evaluation, selection, crossover, and mutation over multiple generations to arrive at a solution.
Genetic programming is an optimization technique inspired by biological evolution that uses techniques like mutation, crossover, fitness functions and multiple generations to evolve programs that can solve complex problems. It works by initializing a random population of computer programs and then applying genetic operations to the fittest programs each generation to evolve increasingly better solutions over time without needing human guidance. Common applications of genetic programming include automatic programming, symbolic regression, data modeling, engineering design, game strategies and more.
Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document discusses software module clustering using genetic algorithms and hill climbing techniques. It introduces genetic algorithms and hill climbing algorithms and how they can be applied to software module clustering. Specifically, it proposes using multiple hill climbs first to gather information about the search landscape, which is then used to define "building blocks" to improve subsequent searches done by genetic algorithms. The results of empirical studies using this novel approach show it to be effective at software module clustering.
Toward a Natural Genetic / Evolutionary Algorithm for Multiobjective Optimiza...Startup
New Genetic or Evolutionary Algorithm for multiobjective optimization, that attempts to find tradeoff solutions and scales easily with increase in parameter space as well as objective space. Does not use complex niche calculation that is used in existing multiobjective genetic algorithms.
This document summarizes a research paper that proposes using a genetic algorithm to solve the travelling salesman problem (TSP). It begins by defining the TSP and explaining that it is NP-hard. The document then reviews various existing approaches that have used genetic algorithms and other metaheuristics to solve TSP. It proposes a genetic algorithm with tournament selection, two-point crossover, and interchange mutation operators. The algorithm is tested on sample problems with 15 cities and is shown to find optimal or near-optimal solutions. In conclusion, the document argues that genetic algorithms can efficiently find good solutions to TSP, especially when combined with knowledge from heuristic methods.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. It also mean that it make best use of a situation or resource. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense. Practice of optimization is restricted by the lack of full information, and the lack of time to evaluate what information is available (see bounded reality for details). In computer simulation (modeling) of business problems, optimization is achieved usually by using linear programming techniques of operations research.
The document discusses the traveling salesman problem (TSP) which aims to find the shortest route for a salesman to visit each city once and return to the starting city. While computers can solve TSP problems, the time required increases enormously with the number of cities - solving a 15,000 city problem took over 20 years, and an 85,000 city problem would take over 130 computer years. Therefore, exact solutions are only efficient for small problems, as increasing the problem size significantly lengthens the computation time required.
The document discusses the Travelling Salesman Problem (TSP), which aims to find the shortest route to visit each city in a list exactly once and return to the origin city. It describes TSP as an NP-hard problem, belonging to the complexity class NP-complete. The document provides background on TSP, explaining it cannot be solved in polynomial time using techniques like linear programming. While an efficient solution to the general TSP has not been found, there are approximation algorithms that provide near-optimal solutions.
This document discusses using a hill climbing search algorithm to solve the traveling salesman problem (TSP). It begins by defining the TSP problem and explaining that it is NP-Hard. It then introduces stochastic optimization methods, including hill climbing, which take randomly generated routes and incrementally improve them. Hill climbing works by only taking steps that improve the current solution until no better steps can be found, risking getting stuck at local maxima.
Travelling salesman problem using genetic algorithms Shivank Shah
This document describes using a genetic algorithm to solve the traveling salesman problem. It defines the traveling salesman problem as finding the shortest route for a salesman to visit each city once and return to their starting city. The method uses a genetic algorithm with operations like generating a random initial population, calculating fitness, selection for crossover using probabilities, crossover using techniques like PMX, and mutation techniques like swapping or flipping parts of routes. The goal is to evolve routes with shorter distances over multiple generations to minimize the total travel distance.
09 genetic algorithms by Priyesh Marvipriyeshmarvi
Genetic algorithms (GAs) are adaptive heuristic search algorithms based on Darwinian evolution. GAs represent an intelligent exploitation of random search used to solve optimization problems. In GAs, competition among potential solutions for limited resources results in fitter solutions dominating over weaker ones. Unlike older AI systems, GAs are more robust and perform well in large, complex search spaces where optimal solutions may not be found through other techniques. GAs navigate huge search spaces, looking for optimal combinations one may not otherwise discover in a lifetime.
3 mathematical priliminaries DATA compressionShubham Jain
The document discusses different methods of data compression by modeling redundancy in data. It provides three examples: (1) exploiting a linear pattern in data points to compress to 2 bits per sample instead of 5 bits; (2) assigning shorter codes to more frequent symbols in a sequence to compress to 2.58 bits per symbol from 3 bits; and (3) using entropy coding which assigns codes based on symbol probabilities to maximize compression. The goal is to remove redundancy while preserving information content.
Genetic algorithms are a search heuristic and optimization technique based on natural evolution. They use processes such as inheritance, mutation, and natural selection to evolve solutions to problems iteratively. A genetic algorithm begins with a random population which is evaluated and selected through fitness. Offspring are created through crossover and mutation of parents over generations, with less fit solutions dropping out in a survival of the fittest manner, until a satisfactory solution is found. Genetic algorithms have been applied to a variety of optimization and search problems in many different domains.
Dynamic Programming design technique is one of the fundamental algorithm design techniques, and possibly one of the ones that are hardest to master for those who did not study it formally. In these slides (which are continuation of part 1 slides), we cover two problems: maximum value contiguous subarray, and maximum increasing subsequence.
NP completeness. Classes P and NP are two frequently studied classes of problems in computer science. Class P is the set of all problems that can be solved by a deterministic Turing machine in polynomial time.
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Edward Tufte and Information Design for the Websprocketeer
The document discusses Edward Tufte's principles of information design and their application to web design. It outlines five of Tufte's principles: 1) Graphics reveal data 2) Add detail to clarify 3) Use small multiples to invite comparisons 4) Employ the "smallest effective difference" 5) Above all else, show the data. It then explains how each of these principles can be applied to web design, providing examples and quotes from other designers.
The Traveling Salesman Problem (TSP) involves finding the minimum cost tour that visits each customer exactly once and returns to the starting depot. Key heuristics to solve the TSP include nearest neighbor, insertion methods, and 2-opt exchanges. The Vehicle Routing Problem (VRP) extends the TSP by routing multiple vehicles of limited capacity from a central depot to serve customer demands. Common heuristics for the VRP include savings algorithms and sweep methods.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
Genetic algorithms are adaptive heuristic search algorithms based on Darwinian principles of natural selection and genetics. They represent an intelligent exploitation of random search used to solve optimization problems. The document discusses the biological background of genetic algorithms, including chromosomes, genes, alleles, and evolution. It also covers the basic concepts of genetic algorithms such as representation of solutions, fitness functions, selection, crossover and mutation operators.
This is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.
For other resources visit: http://pimpalepatil.googlepages.com/
For more information mail me on pbpimpale@gmail.com
This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The Optimizing Multiple Travelling Salesman Problem Using Genetic Algorithmijsrd.com
The traveling salesman problem (TSP) supports the idea of a single salesperson traveling in a continuous trip visiting all n cities exactly once and returning to the starting point. The multiple traveling salesman problems (mTSP) is complex combinatorial optimization problem, which is a generalization of the well-known Travelling Salesman Problem (TSP), where one or more salesman can be used in the path. In this paper mTSP has also been studied and solved with GA in the form of the vehicle scheduling problem. The existing model is new models are compared to both mathematically and experimentally. This work presents a chromosome methodology setting up the MTSP to be solved using a GA.
The document discusses ant colony optimization, a nature-inspired metaheuristic algorithm. It describes how real ants communicate indirectly via pheromone trails to find the shortest path between their nest and food sources. The algorithm mimics this behavior to solve complex optimization problems like the traveling salesman problem, with artificial "ants" probabilistically building solutions through pheromone-guided movements.
In this work, the hybrid techniques of genetic algorithm (GA) and particle swarm optimization (PSO) are presented. PSO and GA are two population-based heuristic search methods that can be applied to the channel allocation optimization problem. GAPSO is based on a mixture of particle swarm optimization (PSO) and genetic algorithms (GA). Individuals of a new generation are produced in GAPSO by PSO in addition to crossover and mutation operations as in GA. In order to reduce the number of blocked calls and handoff failures in the mobile network, the Hybrid GAPSO algorithm is used to allocate tasks to resources efficiently. The proposed strategy optimizes the channel allocation using the GAPSO.
In this work, the hybrid techniques of genetic algorithm (GA) and particle swarm optimization (PSO) are
presented. PSO and GA are two population-based heuristic search methods that can be applied to the
channel allocation optimization problem. GAPSO is based on a mixture of particle swarm optimization
(PSO) and genetic algorithms (GA). Individuals of a new generation are produced in GAPSO by PSO in
addition to crossover and mutation operations as in GA. In order to reduce the number of blocked calls
and handoff failures in the mobile network, the Hybrid GAPSO algorithm is used to allocate tasks to
resources efficiently. The proposed strategy optimizes the channel allocation using the GAPSO.
Genetic algorithms are a type of optimization technique based on principles of genetics and natural selection. They are commonly used to find optimal or near-optimal solutions to complex problems. A genetic algorithm works by generating an initial population of solutions randomly and then applying genetic operators like selection, crossover and mutation to produce new solutions over successive generations. The fittest solutions survive and less fit solutions are removed, causing the overall population to evolve toward an optimal solution. Key components of a genetic algorithm include encoding potential solutions, selecting parents for mating based on fitness, recombining parents to produce offspring, and introducing random mutations. Genetic algorithms terminate when a maximum number of generations is reached, a fitness threshold is met, or there is no improvement over successive generations
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
Genetic algorithms are a type of optimization algorithm that is inspired by biological evolution. They work by repeatedly modifying a population of candidate solutions and selecting the fittest solutions to breed a new generation. There are four main types of genetic algorithms: generational GAs, steady-state GAs, steady-generational GAs, and (μ + μ)-GAs. Genetic algorithms are commonly used to find high-quality solutions to optimization problems and can search very large spaces, but can also be computationally expensive.
This document provides an overview of optimization techniques used in machine learning, specifically genetic algorithms. It describes the basic concepts of genetic algorithms including genetic operators like selection, crossover, and mutation. It also discusses genetic programming and how programs can be represented as trees or sequences. Finally, it covers Markov decision processes and how they can be used to model sequential decision making problems.
1) The document presents an approach to solving the inverse kinematics problem of robotic manipulators using genetic algorithms.
2) Genetic algorithms are applied by encoding joint angles into chromosomes and evaluating fitness based on end-effector position and orientation accuracy.
3) The approach handles redundancies and singularities effectively and can compute motions for manipulators to follow specified end-effector paths.
GARS is a genetic algorithm that uses reserve selection to overcome premature convergence. It divides the population into a non-reserved area and reserved area. The non-reserved area uses tournament selection while the reserved area maintains diversity by selecting less fit individuals not chosen for the non-reserved area. Experimental results on benchmark optimization problems show GARS achieves better results than a conventional genetic algorithm, demonstrating its ability to maintain diversity and avoid getting trapped in local optima.
The document discusses binary genetic algorithms. It begins by motivating GAs as able to find good enough solutions fast enough compared to traditional methods. It then describes GAs as optimization techniques based on genetics and natural selection. The key steps of a binary GA are described as population initialization, fitness calculation, selection, crossover, mutation, and termination. Example applications like the travelling salesman problem are provided. Advantages include not requiring derivatives, always finding an answer, and being faster than traditional methods. Limitations include high computational cost of fitness calculations and potential lack of convergence to the optimal solution.
This document summarizes a research article from the International Journal of Electronics and Communication Engineering & Technology. The article compares the performance of three genetic algorithm crossover operators - PMX, OX, and CX - for solving the Traveling Salesman Problem (TSP). It finds that the PMX operator enables achieving a better solution than the other two operators tested. The document provides background on genetic algorithms and describes the TSP optimization problem, literature on using genetic algorithms for TSP, and proposes a new PMX crossover scheme to resolve TSP more efficiently.
A Comparative Analysis of Genetic Algorithm Selection TechniquesIRJET Journal
This document compares different selection techniques used in genetic algorithms. It discusses roulette wheel selection, rank selection, tournament selection, elitism, and steady-state selection. Roulette wheel selection chooses parents based on their fitness, with better fitness having more chances to be selected. Rank selection assigns ranks to the population before selection. Tournament selection randomly chooses individuals and selects the fitter one. Elitism selects the most fit individuals as parents. Steady-state selection keeps most of the population intact between generations. The document provides pros and cons of each technique and concludes with an analysis of their effects on genetic algorithm performance and diversity.
A Survey of Solving Travelling Salesman Problem using Ant Colony OptimizationIRJET Journal
This document summarizes research on solving the travelling salesman problem (TSP) using ant colony optimization (ACO). It first provides background on TSP and describes how ACO mimics real ants finding food to solve optimization problems. The document then reviews several papers that have applied ACO to TSP and compared it to other algorithms. It finds that ACO generally performs better than genetic algorithms at finding optimal solutions to TSP as the number of cities increases. Finally, it proposes studying the effects of different ACO parameters on finding optimal TSP solutions.
Comparison Study of Multiple Traveling Salesmen Problem using Genetic AlgorithmIOSR Journals
This document compares solving the multiple traveling salesman problem (MTSP) using a genetic algorithm. MTSP is an extension of the traveling salesman problem where multiple salesmen must visit cities and return to a depot. The genetic algorithm represents solutions as sequences of cities visited and uses crossover and mutation operators to evolve better solutions. Experimental results on different datasets show the genetic algorithm can find good quality MTSP solutions in reasonable time, especially for large problems.
This document discusses genetic algorithms and their components. It begins by explaining that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that uses techniques like inheritance, mutation, selection, and crossover. It then defines the key terms used in genetic algorithms, such as individuals, populations, chromosomes, genes, and fitness functions. The rest of the document provides more details on genetic algorithm components like representation of solutions, selection of individuals, crossover and mutation operations, and the general genetic algorithm process.
This document describes a genetic algorithm for finding the shortest path or tour in the traveling salesman problem (TSP). It introduces genetic algorithms and describes how they are applied to the TSP. The fitness measure calculates the total distance of a tour. Selection uses steady-state selection and crossover uses partially mapped crossover. Mutation uses swap mutation. The overall procedure initializes a population randomly, evaluates fitness, performs crossover and mutation, selects the next generation, and iterates until stopping criteria is met, outputting the best solution found. Experimental results on problem sizes of 10, 20, and 30 cities show the best and average tours found.
Similar to Muzammil Adulrahman ppt on travelling salesman Problem Based On Mutation Genetic Algorithms (20)
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
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van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
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• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
2. The task of finding the shortest possible path that visits each city
exactly once and returns to the initial city has been suggested by
many scholars.
Travelling Salesman Problem (TSP) is among the extensively
studied optimization problem that has been used to find the
shortest possible route.
The TSP has many applications including the following :
Manufacture of microchips
The routing of trucks for packet post pickup
Packet routing in GSM
The delivery of meals to home bound persons etc.
3. GA at a glANCE
GA is an empirical search that mimics the process of natural
evolution
GA generate solutions to optimization problems using
techniques inspired by natural evolution, such as inheritance,,
mutation, selection and crossover
In GA a space of hypotheses is searched to identify the best
hypothesis.
The best hypothesis is defined as the one that optimizes a
predefined numerical measure for the problem at hand, called
the hypothesis fitness
GA operates by iteratively updating a pool of hypotheses, called
the population
4. Select: Randomly select members of Population
Crossover: Randomly select pairs of hypotheses from P, to
produce offspring by applying the Crossover operator. Add all
offspring to new P1.
Mutate: Choose m percent of the members of P, with uniform
probability. For each, invert one randomly selected bit in its
representation.
Update: P ← P1.
Evaluate: compute Fitness function
Return the hypothesis from P that has the highest fitness.
5. Genetic Algorithm is another technique which can also find
solution to TSP due to the following reasons :
Due to their flexibility and robustness
They are also readily amenable to parallel implementation
They are able to solve problems knowing nothing about the
problem from the start
6. Population Size - The population size is the initial number of
random tours that are created when the algorithm starts.
Neighborhood / Group Size – In each generation the best 2 tours
are the parents. The worst 2 tours get replaced by the children.
Mutation % - The percentage that each child after crossover will
undergo mutation when a tour is mutated.
Nearby Cities - As part of a greedy initial population, the GA will
prefer to link cities that are close to each other to make the initial
tours.
Nearby City Odds % - This is the percent chance that any one
link in a random tour in the initial population will prefer to use a
nearby city instead of a completely random city.
6. Maximum Generations – Number of crossovers are run before
the algorithm is terminated
7. Fıtness functıon= Least tour dıstance ın a group.
Selectıon method- Determınıstıcs wıth a probabılıty of 1.
Cross over- skıpped.
Mutatıon:
Recıprocal exchange Mutatıon- Two cıtıes are randomly selected and
theır posıtıons ın chromosomes are exchanged.
Flıp Mutatıon- The two cıtıes selected are flıpped over, example ıf theır
are sıx cıtıes 1, 2, 3, 4, 5, 6 ın the chromosomes and cıtıes at posıtıon 2
and 5 are chosen as a mutatıon poınts, then the new chromosomes
after flıpıng posıtıon the gıven posıtıons are 1, 5, 4, 3, 2, 6.
Backward slıde Mutatıon- As the name ımplıes, two mutatıon posıtıons
are move to the next posıtıons ın a backward dırectıonwıthın the span
of the Mutatıon poınts. Example ıf the above cıtıes posıtıon ın the
chromosomes are used and posıtıon 2 and 5 are slıded then the cıtıes
posıtıon ın the new chromosomes are1, 3, 4, 5, 2, 6.
8. ALGORITHM
Inıtıalıze the populatıon
Randomly generate the populatıon members.
Calculate the total dıstance for each tour.
Evaluate each tour fıtness ın each group.
Select the tour wıth the least dıstance ıe hıghest fıtness.
Apply Mutatıon to the best offsrıng to get the three new routes.
Set the best route as your new global mınımızer
Iterate whıle number of ıteratıon ıs less than the maxımum
ıteratıon untıl the optımal route ıs dıscovered (convergence
poınt).
Stop.
9. An N by N distance matrix was used where N stands for the
number of cities.
All the cities were assumed to be points in space and their
respective Euclidean distance were computed using the
Euclidean equations to get the inputs of the distance matrix
(Dmat).
For a real and more practical situation, the exact distances
between the cities in consideration can be directly inputed into
the distance matrix.
In asymmetric TSP the approach mentioned might not work
since the distance travelled to get to city B from A might not
necesssarily be the same when coming back to A from B.
10. The simulation results showed that with a higher number of
iterations a better route is discovered but it takes more time to
converge to an optimal solution.
With lower population size and a less number of cities little time
is required to get the optimal route.
The optimal tour distance at a given population and iteration
might vary when the same population size and iteration number
is used at a different run.
It is so because the vertexes of the cities used in Euclidean
computation are randomly generated.
It can also be proven that to get the best population size that
takes little time, a range within Number of Cities * 3 <
Population Size < Number of Cities * 5 should be used as
suggested by by Nilesh Gambhava and Gopi Sanghani in their
papers. Below are the figures of simulation result at different
instances.
13. Genetic algorithm has been quite an exciting tool for solving
optimization problem.
Its flexibility is astonishingly remarkable. This paper has
indicated that mutation in genetic is a powerful operator which
makes GA to stand tall among its fellow optimization
algorithms.
The Mutation operator ensures that trap of local minimum is
avoided which is one of the major advantage of GA.
With a better manupilation of this tool in a suitable problem, it
is always possible that GA will remained in the mainstrem in the
field of optimization.