These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Models of Operations Research is addressedSundar B N
Introduction, Meaning and Characteristics of Operations Research is addressed.
MODELS IN OPERATIONS RESEARCH, Classification of Models, degree of abstraction, Purpose Models, Predictive models, Descriptive models, Prescriptive models, Mathematic / Symbolic models, Models by nature of an environment, Models by the extent of generality, Models by Behaviour, Models by Method of Solution, Models by Method of Solution, Static and dynamic models, Iconic models Iconic models, Analogue models.
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Software Process Control on Ungrouped Data: Log-Power ModelWaqas Tariq
Statistical Process Control (SPC) is the best choice to monitor software reliability process. It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of failures which is ungrouped data using an infinite failure mean value function of Log-Power model, which is Non-Homogenous Poisson Process (NHPP) based. The Maximum Likelihood Estimation (MLE) approach is used to estimate the unknown parameters of the model.
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
Models of Operations Research is addressedSundar B N
Introduction, Meaning and Characteristics of Operations Research is addressed.
MODELS IN OPERATIONS RESEARCH, Classification of Models, degree of abstraction, Purpose Models, Predictive models, Descriptive models, Prescriptive models, Mathematic / Symbolic models, Models by nature of an environment, Models by the extent of generality, Models by Behaviour, Models by Method of Solution, Models by Method of Solution, Static and dynamic models, Iconic models Iconic models, Analogue models.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Software Process Control on Ungrouped Data: Log-Power ModelWaqas Tariq
Statistical Process Control (SPC) is the best choice to monitor software reliability process. It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of failures which is ungrouped data using an infinite failure mean value function of Log-Power model, which is Non-Homogenous Poisson Process (NHPP) based. The Maximum Likelihood Estimation (MLE) approach is used to estimate the unknown parameters of the model.
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
Multi-objective Optimization of PID Controller using Pareto-based Surrogate ...IJECEIAES
Most control engineering problems are characterized by several objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto-optimal solutions. This paper proposed a Paretobased Surrogate Modeling Algorithm (PSMA) approach using a combination of Surrogate Modeling (SM) optimization and Pareto-optimal solution to find a fixed-gain, discrete-time Proportional Integral Derivative (PID) controller for a Multi Input Multi Output (MIMO) Forced Circulation Evaporator (FCE) process plant. Experimental results show that a multi-objective, PSMA search was able to give a good approximation to the optimum controller parameters in this case. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) method was also used to optimize the controller parameters and as comparison with PSMA.
Integrating Fuzzy Dematel and SMAA-2 for Maintenance Expensesinventionjournals
The majority of the allowances being transferred to public institutions are mostly spent for buying new equipment, materials, facilities and their maintenance and repair. Some of the public sectors establish their own plants in order to reduce the maintenance and repair costs and gain ability to perform these activities. However, developing technology and variety of materials make their repair and maintenance activities more expensive for them. In this study, vital criteria for a public institution are determined. By using Fuzzy DEMATEL (Decision Making Trial And Evaluation Laboratory) method the degree of importance is identified by two defuzzification methods and the alternatives are ranked by using SMAA-2 (Stochastic Multi Criteria Acceptability Analysis) in three scenarios. The results show that different defuzzification methods change the order of preferences.
A Novel Hybrid Voter Using Genetic Algorithm and Performance HistoryWaqas Tariq
Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters.
AN IMPROVE OBJECT-ORIENTED APPROACH FOR MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCH...ijcsit
Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing subproblem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
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# Students can catch up on notes they missed because of an absence.
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Our Vision & Mission – Simplifying Students Life
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Multitasking (often referred to as timesharing) has been extensi vely studied from a mental workload and human performance perspective. However,a relatively small amount of r esearch has been conducted in the manufacturing domain (Wickens,1992). As the level of system automation increases,the role of the human has shifted from that of a manual controller to system supervisor (Sheridan and Johannsen,1976). According to Sheridan (1994),�human operators in AMS make their way among machines,inspecting parts,observing displays,and modifying control settings or keying in commands,most of it through computer-medi ated control panels adjacent to various machines.� This role of human operators in AMS has been identified as supervisory control in this paper.
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
Multi-objective Optimization of PID Controller using Pareto-based Surrogate ...IJECEIAES
Most control engineering problems are characterized by several objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto-optimal solutions. This paper proposed a Paretobased Surrogate Modeling Algorithm (PSMA) approach using a combination of Surrogate Modeling (SM) optimization and Pareto-optimal solution to find a fixed-gain, discrete-time Proportional Integral Derivative (PID) controller for a Multi Input Multi Output (MIMO) Forced Circulation Evaporator (FCE) process plant. Experimental results show that a multi-objective, PSMA search was able to give a good approximation to the optimum controller parameters in this case. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) method was also used to optimize the controller parameters and as comparison with PSMA.
Integrating Fuzzy Dematel and SMAA-2 for Maintenance Expensesinventionjournals
The majority of the allowances being transferred to public institutions are mostly spent for buying new equipment, materials, facilities and their maintenance and repair. Some of the public sectors establish their own plants in order to reduce the maintenance and repair costs and gain ability to perform these activities. However, developing technology and variety of materials make their repair and maintenance activities more expensive for them. In this study, vital criteria for a public institution are determined. By using Fuzzy DEMATEL (Decision Making Trial And Evaluation Laboratory) method the degree of importance is identified by two defuzzification methods and the alternatives are ranked by using SMAA-2 (Stochastic Multi Criteria Acceptability Analysis) in three scenarios. The results show that different defuzzification methods change the order of preferences.
A Novel Hybrid Voter Using Genetic Algorithm and Performance HistoryWaqas Tariq
Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters.
AN IMPROVE OBJECT-ORIENTED APPROACH FOR MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCH...ijcsit
Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing subproblem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Multitasking (often referred to as timesharing) has been extensi vely studied from a mental workload and human performance perspective. However,a relatively small amount of r esearch has been conducted in the manufacturing domain (Wickens,1992). As the level of system automation increases,the role of the human has shifted from that of a manual controller to system supervisor (Sheridan and Johannsen,1976). According to Sheridan (1994),�human operators in AMS make their way among machines,inspecting parts,observing displays,and modifying control settings or keying in commands,most of it through computer-medi ated control panels adjacent to various machines.� This role of human operators in AMS has been identified as supervisory control in this paper.
If you love chocolate and are looking for any excuse to eat it, you probably are a chocoholic. Did you know that there are heart health benefits to eating chocolate? While chocolate hasn’t gained the status as a health food quite yet, its reputation is on the rise and more and more studies suggest it can provide benefits to your heart. But don't forget, everything in moderation.
View our slide show to learn more about “chocolate therapy”.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Adapted Branch-and-Bound Algorithm Using SVM With Model SelectionIJECEIAES
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
Integrated bio-search approaches with multi-objective algorithms for optimiza...TELKOMNIKA JOURNAL
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
In this Machine Learning tutorial, we will cover the top Neural Network Algorithms. These algorithms are used to train the Artificial Neural Network. This blog provides you a deep learning of the Gradient Descent, Evolutionary Algorithms, and Genetic Algorithm in Neural Network.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
4. Chapter 1
Introduction
This elaboration which is created in the context of the course ”Evolutionary
Algorithms” held at the University of Paderborn covers the field of self-adaptive
Evolutionary Algorithms (EAs).
1.1 Motivation
Many parameters are used in a variety of Evolutionary Algorithms. Some of
them are the initial population size, the number of crossover points, the proba-
bilities for the mutation or crossover. Each of these parameters are often tuned
and adjusted ”by hand”. Typically one parameter is adjusted at a time, which
may lead to sub-optimal choices, since often it is not known how the parameters
interact. Parallel tuning of multiple parameters cause the amount of tests to
rise exponentially.
Another approach is to transfer the results for the parameters of a given
problem to a new similar one. But there is no guarantee that this approach
is viable. The rigid form of static parameters contradicts the dynamic nature
of EAs. E.g. a large mutation step in the early generations may lead to a
faster approximation and a small step in the late generation to a more accurate
solution. This is not possible with fixed parameters.
The nature of the parameter setting problem is typical for problems solved
by EAs1. So an intuitive approach is to evolve the parameters with the algo-
rithm, too. Many different procedures were researched to adapt the parameters
and the parameter choice differs strongly from case to case, but the main idea is
to no longer choose the parameters semi-arbitrarily2 but to let the parameters
adapt themselves to the problem.
1
It qualifies as an optimization problem.
2
The choices were often made from experience.
1
5. 2 CHAPTER 1. INTRODUCTION
1.2 Overview
This work is organized as follows. The next chapter gives a short introduction
to evolutionary algorithms followed by the definition of terms used to describe
self-adaptive EAs. The chapter ends with examples of the use of self-adaptive
parameters in previous work. Chapter 3 gives a summary and ends with future
prospects.
6. Chapter 2
Evolutionary Algorithms
In the first part of this chapter I will describe the principles behind Evolutionary
Algorithms. This description will be followed by the idea of self-adaptiveness
and its application to EAs. The following part of this chapter give examples
of self-adaptive EAs which concentrate on different operators. The last part
considers the combination of parameter adaption.
2.1 Overview
Evolutionary computation and Evolutionary Algorithms use computational mod-
els of evolutionary processes from biology as key elements in the design and
implementation of computer-based problem solving systems. They take their
inspiration from natural selection and survival of the fittest. EAs differ from
more traditional optimization techniques in that they involve a search from a
population of solutions, not from a single point. Each iteration of an EA usually
involves a competitive selection that weeds out poor solutions. The solutions
with high fitness are recombined with other solutions by swapping parts of a
solution with another. Solutions are also mutated by making a small more or
less random change to a single element of the solution. Recombination and
mutation are used to generate new solutions that are biased towards regions
of the search space for which good solutions have already been seen. Figure
2.1 shows the usual approach to reflect the Evolution from biology to computer
science.
Several different types of evolutionary search methods were developed indepen-
dently. These include
• Genetic Programming (GP)
• Evolutionary Programming (EP)
• Evolutionary Strategies (ES)
• Genetic Algorithms (GA)
3
7. 4 CHAPTER 2. EVOLUTIONARY ALGORITHMS
Figure 2.1: Most Evolutionary Algorithms use this sequence of steps: Selection,
Reproduction, Mutation and Evaluation.
Genetic Programming creates and evolves programs. They are well suited
for problems that require the determination of a function that can be simply
expressed in a function form. Evolutionary Programming focuses on optimizing
continuous functions without recombination while Evolutionary Strategies focus
on optimizing continuous functions with recombination. Both are well suited for
optimizing continuous functions. They use a real valued representation [Fog97].
Genetic Algorithms focus on optimizing general combinatorial problems though
they have occasionally been applied to continuous problems, too. They usually
use a binary representation.
The different steps in a typical EA cycle, (Selection, Reproduction, Muta-
tion and Evaluation) each have many parameters. Questions arise like how
many individuals should the initial population have, which crossover operator
to choose (e.g. uniform vs. 2-point crossover) or what value should the muta-
tion probability have. These parameters are usually chosen experimentally and
given to the algorithm ”by hand”1. The quality of the algorithm relies on the
quality of these parameters. The better the parameters are the faster an EA
reaches a solution, the better it can handle difficult situations2 and it may even
deliver better results.
Determining good parameters is a very time-consuming task. Often many
iterations are needed before e.g. a good mutation rate is found as the param-
eters are problem dependent and they can even interact between themselves,
complicating the search. Also the problem description often does not give clues
e.g. to how large a population should be.
The attributes of the search for good parameters qualifies it as an EA prob-
lem. Therefore attempts were made to apply EAs to its own parameters. This
1
The algorithm is initialized by a config file e.g.
2
Local minima in a minimization problem
8. 2.2. SELF-ADAPTIVENESS 5
idea is called parameter control and explained in the following section.
2.2 Self-adaptiveness
Before I go into detail how self-adaptiveness works, I want to define and classify
this term. In [EHM99] Eiben, Hinterding and Michalewicz give a terminology
which I will use throughout this elaboration.
In the choice of parameters they distinguish two major forms: the parameter
tuning and parameter control. Parameter tuning describes the process of setting
parameters before the run of the EA while parameter control defers to changing
the parameters during the run. They subclassify the parameter control into
three categories: deterministic, adaptive and self-adaptive parameter control
(see Figure 2.2).
Figure 2.2: Classification of methods how parameters are chosen. Parameter tun-
ing refers to changing parameters ”by hand”, while parameter control refers to set-
ting the parameters e.g. based on some function which is dependent on a algorithm
variable like the number of generations.
In the following three sections I will give examples to each of these parameter
control approaches on the basis of mutation step size parameter with a Gaussian
mutation which uses the normal distribution of N(0, σ). The mean is set to zero
and the standard deviation of σ can be interpreted as the mutation step size.
The mutation is applied as follows:
xi = xi + N(0, σ)
where xi is each component of an individual x.
2.2.1 Deterministic parameter control
The first possibility for parameter control is to parameterize the σ with a vari-
able t leading to a function σ(t). Now we get the possibility to change the σ
during the run. As is shown in [B¨ac92] this may improve the algorithm speed.
One possible choice of t could be the generation number: the longer the algo-
rithm runs the smaller values for the mutation step size it gets. The idea is that
9. 6 CHAPTER 2. EVOLUTIONARY ALGORITHMS
at the beginning of the algorithm we want a high diversity of the individuals
in order not to miss some solutions. We want a higher convergence in later
generations, so a smaller value for σ(t) can be chosen.
Still this approach needs much input from outside3. The function σ(t) is
determined for each value of t and predictable.
Deterministic parameter control modifies the parameter without
using any feedback from the search.
2.2.2 Adaptive parameter control
One step further in letting the algorithm find parameter values for itself is to
incorporate information from the search process into the mutation step size σ.
In [Rec73] the ’1/5 success rule’ for (1+1)-evolution strategies is presented. It
states that the ratio of successful mutations to all mutations should be 1/5. If
the ratio is greater then the mutation step size should be increased, and vice
versa. A successful mutation is a mutation which produces an offspring that
is better than its parent. The ratio is determined as an average of mutations
after a fixed number of generations. This approach is still heuristic but yet the
σ(t) is not deterministic.
Adaptive parameter control modifies the parameter with some feed-
back from the search (usually using a heuristic).
2.2.3 Self-adaptive parameter control
The main idea to let the algorithm set its own parameters can be implemented
as follows. Assume that an individual has the following form:
< x1, x2, ..., xi >
The mutation step size can be included in the individual itself as a gene re-
sulting in the following form:
< x1, x2, ..., xi, σ >
This additional gene is transformed during the mutation, too. It undergoes
an evolution similar to the individual. Usually the e function is used to trans-
form the σ value:
σi = σi ∗ eN(0,τ0)
xi = xi + N(0, σi)
3
This approach is also called extrinsic evolution.
10. 2.3. EXAMPLES OF SELF-ADAPTIVE EVOLUTIONARY ALGORITHMS7
Through this self-adaption no input is needed for the parameter and the
values are set by the algorithm itself. Each individual has its own σ. Another
finer approach is to give each gene its own σ leading to the following represen-
tation:
< x1, x2, ..., xi, σ1, σ2, ..., σi >
In this form each gene gets its own step size and the individuals get a larger
freedom grade in adapting itself to the shape of the fitness function.
What has to be noticed is that the adaption of the parameter (the mutation
step size here) happens before the fitness is given to it. That means that getting
a good parameter doesn’t rise the individual’s fitness but only its performance
over time.
Self-adaptive parameter control encodes the parameter within each
individual evolving it with the individual.
2.3 Examples of self-adaptive Evolutionary Algorithms
In this part of this chapter I will describe some examples of self-adaptive evo-
lutionary algorithms. The first section describes different approaches to self-
adaption where different operators are used. In the last section an example of
a combination of different self-adapting parameters is given.
2.3.1 Self-adaptive Evolutionary Algorithms adapting one pa-
rameter
An interesting approach to a self-adaptive crossover operator in a genetic algo-
rithm is found in [Spe95]. Spears decided to let the GA be self-selective with
respect to its choice of crossover operator. He argues that as 2-point crossover
is the least disruptive4, and uniform crossover the most disruptive operator, it
is reasonable to have the GA select from only those two possibilities. Although
a high disruption may stress exploration at the expense of exploitation, there
are situations in which minimizing disruption hinders the adaptive search pro-
cess by overemphasizing exploitation at the expense of needed exploration. An
example of this is when the population size is too small to provide the necessary
sampling accuracy for complex search spaces [JS91].
The implementation appends one bit to the end of every individual in the
GA population. This bit decides whether it is better to use uniform crossover
or to use 2-point crossover. Also, since the approach is tightly coupled, all
genetic operators are allowed to manipulate this extra bit (including crossover).
There are two possibilities how to use this bit. Local adaption uses this bit
only for two individuals: if both bits are equal, the respective crossover is
performed, if not, a random crossover is chosen. Global adaption uses the last
bits of the population to probabilistically determine which crossover operator
4
His main motivation to let the algorithm decide.
11. 8 CHAPTER 2. EVOLUTIONARY ALGORITHMS
to perform on each individual5. An important result is that local adaption
outperforms global adaption although there is no statistical difference between
both methods. The consequence is to tie the crossover information directly to
the individual using local adaption in order to improve the algorithm.
Another approach is presented in [ESKT97] which uses multi-parent repro-
duction. An adaptive mechanism is used to determine the number of parents
based on subpopulations. The idea is similar to coevolution. Different pools of
individuals (species) search by different strategies6. The adaptive population
redistribution is designed to grow successful species and shrink the others. The
crossover operator used is N-parent diagonal crossover (see Figure 2.3).
Figure 2.3: 3-parent diagonal crossover with three children (left) and one child (right).
The results were two edged. On the one hand the experiments have shown that
multi-parent crossover is superior to traditional two-parent crossover. On the
other hand the adaptive mechanism was not able to reward better crossovers
according to their performance. Yet the algorithm was comparable in perfor-
mance to the non-adaptive variant and thus made time consuming comparisons
in search of the best operator unnecessary, which is, as stated in the introduc-
tion, a goal of self-adaptive EAs.
A meta approach was used by [Gre86]. The parameter population size was
determined by another genetic algorithm. So each generation of the meta-GA
set off a whole run of the actual GA with adapted parameters. The outcome
was that the optimal population size for the actual algorithm was somewhere
between 30 and 50 individuals.
In a system where the search space is bounded by constraints the adaption
of the evaluation function has been tested. Assume the evaluation function has
the following form:
eval(x) = f(x) + W ∗ penalty(x)
5
If 10 of 100 individuals have the uniform crossover bit set, then each individual has the
probability of 10% to use uniform crossover.
6
In this case the species differ only in the crossover operator.
12. 2.3. EXAMPLES OF SELF-ADAPTIVE EVOLUTIONARY ALGORITHMS9
where W is a weight which determines how strong the individual is penalized
if it violates a constraint. The value of W can be adapted in a similar fashion
as the standard deviation σ described earlier.
A typical self-adaptive parameter for evolutionary strategies, the mutation
rate, was also self-adapted in a GA by B¨ack [B¨ac92]. He uses a extended
bitstring representation of the mutation probability pm, appended to each indi-
vidual. Each mutation rate is applied to itself and to each individual gene (see
Figure 2.4). The experiments show that a significant improvement is reached
with this approach over the standard GA without self-adaption.
Figure 2.4: Each mutation rate is applied to itself and to each of it corresponding
genes.
2.3.2 Combining self-adaptive parameters
In [SF96] the representation of an individual is chosen to have four parts7 in or-
der to incorporate both an adaption of the mutation operator and the crossover
operator. Each individual has the usual problem encoding, a mutation rate
and now two additional linkage bits. These bits are interpreted as connecting
points, where new offspring individuals can connect to. These new individuals
are called blocks. The approach is similar to [SM87], where the crossover oper-
ator uses ”punctuation marks” to encode crossover points. It was confined to
only two parents while the blocks benefit from the benefits of multi-parent re-
combination [ERR94] without the necessity of tuning the type of recombination
to the nature of the problem.
An important result from this combined adaption is that the algorithm
does not seem to suffer from a great ”learning overhead” on simple problems
and on more complex functions it discovers significantly higher optima than
the majority of other algorithms. They state that this can attributed to the
7
In a binary representation.
13. 10 CHAPTER 2. EVOLUTIONARY ALGORITHMS
synergistic effects of simultaneously adapting both recombination strategy and
mutation rates.
It is interesting to note that most papers on combining parameters use self-
adaption. In [EHM99] Eiben et al. presume that feedback-based heuristic are
even more difficult to handle than static parameter tuning.
14. Chapter 3
Summary
3.1 Conclusion
The effectiveness of an evolutionary algorithm depends on many factors, e.g.
representation, operators, etc.. The number and variety of the parameters and
the possible choices make a selection of good setting for an EA very difficult.
The ”No-Free-Lunch”-Theorem [WM97] states even that there exists no perfect
EA:
No search algorithm is superior on ALL problems.
A corollary to this theorem is that there is no set of parameters that is
superior on all problems. Yet this theorem assumes no knowledge about the
problem and incorporates ALL problems into its proof, even completely random
ones. So its practical relevance is minimal.
Still adaption provides the opportunity to customize the evolutionary algo-
rithm to the needs of the problem and to change the strategy parameters during
the search. This enables the possibility to incorporate information about the
search space into the algorithm and even let the algorithm select the appropriate
information. The can be considered as two separate searches. The first is the
usual search in the problem space. The second search considers the parameter
space to find an optimal configuration of the EA. Yet most presented algorithm
take only one small part of this space into account, e.g. if the mutation rate
pm is self-adapted it’s only a small part of the parameter space. The other
parameters are still static and tuned. Similarly the meta-GA[Gre86] is confined
to its own search space.
Another advantage of self-adaptive EAs is, that e.g. in mutation rate adap-
tion, each individual gets its own rate, rising the diversity of the population.
This rises the natural drift, meaning that the convergence to local minima is
not as high as it would be with standard EAs. This idea was used in [Spe95], as
the choice between the disruptive uniform crossover corresponds to exploration
of search space or diversity in the population, while the least disruptive 2-point
crossover matches the exploitation1 or the convergence to some solution. In
this way the algorithm adapted itself, whether it should explore or not.
1
E.g. Staying at the same place in the search space, having found a solution.
11
15. 12 CHAPTER 3. SUMMARY
The fact that self-adaptive EAs do not deliver the hoped for outcomes and
complicates the EA let some researchers to disregard it. But often they do not
take the time of parameter tuning into account.
3.2 Future prospects
One of the difficulties of optimizing parameter settings of EAs is that the in-
teractions between these parameters are often unpredictable. Thus the combi-
nation of self-adaptive parameters is an area where research can result in new
insights and improvements to EAs.
Very little runtime analysis has been done on the topic of EAs, and even
less with respect to self-adaptive EAs. A framework where the efficiency of self-
adaptive EAs can be measured an compared is absent. A collection of functions
to optimize is available yet this does not cover all forms of problems covered by
EAs.
There is no treatment about the problems where self-adaption excels and in
which situation it fails, which should be an interesting topic, too.
16. References
[B¨ac92] T. B¨ack. Self-adaptation in genetic algorithms. In Varela and
Bourgine[723], pages 263–271, 1992.
[EHM99] ´Agoston Endre Eiben, Robert Hinterding, and Zbigniew
Michalewicz. Parameter control in evolutionary algorithms.
IEEE Trans. on Evolutionary Computation, 3(2):124–141, 1999.
[ERR94] Agoston E. Eiben, P.-E. Rau´e, and Zs. Ruttkay. Genetic algorithms
with multi-parent recombination. In Yuval Davidor, Hans-Paul
Schwefel, and Reinhard M¨anner, editors, Parallel Problem Solving
from Nature – PPSN III, pages 78–87, Berlin, 1994. Springer.
[ESKT97] A. Eiben, I. Sprinkhuizen-Kuyper, and B. Thijssen. Competing
crossovers in an adaptive ga framework. -, 1997.
[Fog97] David B. Fogel. Evolutionary computation: A new transactions.
IEEE Trans. Evolutionary Computation, 1(1):1–2, 1997.
[Gre86] J Grefenstette. Optimization of control parameters for genetic algo-
rithms. IEEE Trans. Syst. Man Cybern., 16(1):122–128, 1986.
[JS91] Kenneth A. De Jong and William M. Spears. An analysis of of the
interacting roles of population size and crossover in genetic algo-
rithms. In Parallel Problem Solving from Nature - Proceedings of 1st
Workshop, PPSN 1, 1991.
[Rec73] Ingo Rechenberg. Evolutionsstrategie: Optimierung Technischer
Systeme nach Prinzipien der Biologischen Evolution. Fromman-
Holzboog Verlag, 1973.
[SF96] Jim E. Smith and Terence C. Fogarty. Adaptively parameterised
evolutionary systems: Self adaptive recombination and mutation in
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1996), pages 441–450, Berlin, 1996. Springer.
13
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[SM87] J. D. Schaffer and A. Morishima. An adaptive crossover distribution
mechanisms for genetic algorithms. In Grefenstette J. (ed)., edi-
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[Spe95] William Spears. Adapting crossover in a genetic algorithm. In
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[WM97] David H. Wolpert and William G. Macready. No free lunch theorems
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