Evolutionary algorithms are stochastic search and optimization heuristics derived from the classic evolution theory, which are implemented on computers in the majority of cases.
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.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Evolutionary algorithms are stochastic search and optimization heuristics derived from the classic evolution theory, which are implemented on computers in the majority of cases.
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.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
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.
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
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.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
Evolutionary Computing is a research area within computer science. As the name suggest, it is a special flavour of computing, which draws inspiration from the process of natural evolution. The fundamental metaphor of evolutionary computing relates this powerful natural evolution to a particular style of problem solving – that of trial and error.
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.
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
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.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
Evolutionary Computing is a research area within computer science. As the name suggest, it is a special flavour of computing, which draws inspiration from the process of natural evolution. The fundamental metaphor of evolutionary computing relates this powerful natural evolution to a particular style of problem solving – that of trial and error.
List the problems that can be efficiently solved by Evolutionary P.pdfinfomalad
List the problems that can be efficiently solved by Evolutionary Programming?
List the problems that can be efficiently solved by Evolutionary Programming?
Solution
Evolutionary Programming is a Global Optimization algorithm and is an instance of an
Evolutionary Algorithm from the field of Evolutionary Computation. The approach is a sibling
of other Evolutionary Algorithms such as the Genetic Algorithm, and Learning Classifier
Systems. It is sometimes confused with Genetic Programming given the similarity in name, and
more recently it shows a strong functional similarity to Evolution Strategies.
Inspiration
Evolutionary Programming is inspired by the theory of evolution by means of natural selection.
Specifically, the technique is inspired by macro-level or the species-level process of evolution
(phenotype, hereditary, variation) and is not concerned with the genetic mechanisms of evolution
(genome, chromosomes, genes, alleles).
Metaphor
A population of a species reproduce, creating progeny with small phenotypical variation. The
progeny and the parents compete based on their suitability to the environment, where the
generally more fit members constitute the subsequent generation and are provided with the
opportunity to reproduce themselves. This process repeats, improving the adaptive fit between
the species and the environment.
Strategy
The objective of the Evolutionary Programming algorithm is to maximize the suitability of a
collection of candidate solutions in the context of an objective function from the domain. This
objective is pursued by using an adaptive model with surrogates for the processes of evolution,
specifically hereditary (reproduction with variation) under competition. The representation used
for candidate solutions is directly assessable by a cost or objective function from the domain.
Problems for evolutionary algorithms
Evolutionary programming are useful for problems for which no mechanistic method is available
– or when unreasonable assumptions need to be made. Problems for which you cannot calculate
or derive a solution. Problems for which you have to find a solution.
Aspects of problems that lend them to Evolutionary Programming solution include:
Assignment of individuals into groups, wherever simple ranking and truncation will not work.
This usually involves interactions, whereby whether an individual should be in a group depends
on what other individuals will be in that group. Example: developing multiplex groupings for
genotyping.
Problems where thresholds are involved, for example when the value of a solution depends on
whether certain thresholds have been passed, or the fate of an individual depends on passing one
or more thresholds. Example: Supply chain optimizing to target multiple product end-points
and/or turnoff dates.
Combinatorially tedious problems, where many combinations of components can exist. Example:
The setting up of animal matings. Below are the few applications:
Learning classifier systems, or .
Machine learning and reinforcement learningjenil desai
In this slides we have covered all the basics of the machine learning and reinforcement learning. We also covered how mario game can think by himself and complete the game.
An Active Elitism Mechanism for Multi-objective Evolutionary AlgorithmsWaqas Tariq
Classical (or passive) elitism mechanisms in the literature have a holding/sending back structure. In the active elitism mechanism proposed in this paper, a set of elite individuals is excited by genetic operators (crossover/mutation) in archive in the hope of generating better and more diverse individuals than themselves. If a set of excited elites are any better than originals, then archive can be viewed as a place of active solution provider rather than a static storage place. The main motivation behind this approach is that elite individuals are inherently the closest individuals to the solution (of any optimization problem on hand) and exciting those individuals can likely generate more significant outcomes than a far away one. The proposed active elitism mechanism is embedded into well-known multi-objective SPEA and SPEA2 methods (named ACE_SPEA and ACE_SPEA2 respectively) and compared to the original methods using four benchmarks. The active elitist versions of SPEA and SPEA2 maintain better spread and convergence properties than the original methods. The proposed active elitism mechanism can easily be integrated into existing multi-objective evolutionary algorithms.
An Improved Differential Evolution Algorithm for Real Parameter Optimization ...IDES Editor
Differential Evolution (DE) is a powerful yet simple
evolutionary algorithm for optimization of real valued, multi
modal functions. DE is generally considered as a reliable,
accurate and robust optimization technique. However, the
algorithm suffers from premature convergence, slow
convergence rate and large computational time for optimizing
the computationally expensive objective functions. Therefore,
an attempt to speed up DE is considered necessary. This paper
introduces an improved differential evolution (IDE), a
modification to DE that enhances the convergence rate without
compromising with the solution quality. In improved
differential evolution (IDE) algorithm, initial population of
individual is partitioned into several sub-populations, and then
DE algorithm which utilize only one set of population instead
of two as in original DE, is applied to each sub-population
independently. At periodic stages in evolution, the entire
population is shuffled, and then points are reassigned to subpopulations.
The performance of IDE on a test bed of functions
is compared with original DE. It is found that IDE requires
less computational effort to locate global optimal solution.
An Improved Differential Evolution Algorithm for Real Parameter Optimization ...IDES Editor
Differential Evolution (DE) is a powerful yet simple
evolutionary algorithm for optimization of real valued, multi
modal functions. DE is generally considered as a reliable,
accurate and robust optimization technique. However, the
algorithm suffers from premature convergence, slow
convergence rate and large computational time for optimizing
the computationally expensive objective functions. Therefore,
an attempt to speed up DE is considered necessary. This paper
introduces an improved differential evolution (IDE), a
modification to DE that enhances the convergence rate without
compromising with the solution quality. In improved
differential evolution (IDE) algorithm, initial population of
individual is partitioned into several sub-populations, and then
DE algorithm which utilize only one set of population instead
of two as in original DE, is applied to each sub-population
independently. At periodic stages in evolution, the entire
population is shuffled, and then points are reassigned to subpopulations.
The performance of IDE on a test bed of functions
is compared with original DE. It is found that IDE requires
less computational effort to locate global optimal solution.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
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.
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.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
2. Overview
This presentation will provide an overview of
evolutionary computation, and describe several
evolutionary algorithms that are currently of
interest.
Important similarities and differences are noted
upon all the distinct themes of the evolutionary
algorithms which lead to a discussion of
important issues that need to be resolved, and
items for future research.
3. Introduction
Evolutionary computation uses the computational model
of evolutionary processes as key elements in the design
and implementation of computer-based systems and
problem solving applications.
There are a variety of evolutionary computational models
that have been proposed and studied which we will refer
to as evolutionary algorithms.
They share a common conceptual base of simulating the
evolution of individual structures via processes of
selection and reproduction.
They depend on the performance (fitness) of the
individual structures.
4. Evolutionary algorithms (EA)
More precisely, evolutionary algorithms maintain
a population of structures that evolve according
to rules of selection and other operators, such as
recombination and mutation.
Each individual in the population receives a
measure of its fitness in the environment.
Selection focuses attention on high fitness
individuals, thus exploiting the available fitness
information.
5. Evolutionary algorithms (EA)
Recombination and mutation perturb those
individuals, providing general heuristics for
exploration.
Although simplistic from a biologist's
viewpoint, these algorithms are sufficiently
complex to provide robust and powerful
adaptive search mechanisms.
6. Evolutionary algorithms (EA)
A population of individual structures is
initialized and then evolved from
generation to generation by repeated
applications of evaluation, selection,
recombination, and mutation.
The population size N is generally
constant in an evolutionary algorithm.
7. Evolutionary algorithms (EA)
procedure EA
{
t = 0;
initialize population P(t);
evaluate P(t);
until (done) {
t = t + 1;
parent_selection P(t);
recombine P(t);
mutate P(t);
evaluate P(t);
survive P(t);
}
}
8. Evolutionary algorithms (EA)
An evolutionary algorithm typically initializes its
population randomly, although domain specific
knowledge can also be used to bias the search.
Evaluation measures the fitness of each
individual according to its worth in some
environment.
Evaluation may be as simple as computing a
fitness function or as complex as running an
elaborate simulation.
9. Evolutionary algorithms (EA)
Selection is often performed in two steps, parent
selection and survival.
Parent selection decides who becomes parents and how
many children the parents have.
Children are created via recombination, which
exchanges information between parents, and mutation,
which further perturbs the children.
The children are then evaluated. Finally, the survival step
decides who survives in the population.
10. Evolutionary algorithms (EA)
The origins of evolutionary algorithms can
be traced to at least the 1950's.
three methodologies that have emerged in
the last few decades:
"evolutionary programming" (Fogel et al., 1966)
"evolution strategies" (Rechenberg, 1973)
"genetic algorithms” and “genetic
programming” (Holland, 1975).
11. Evolutionary algorithms (EA)
Although similar at the highest level, each of
these varieties implements an evolutionary
algorithm in a different manner.
The differences include almost all aspects of
evolutionary algorithms, including the choices of
representation for the individual structures, types
of selection mechanism used, forms of genetic
operators, and measures of performance.
12. Evolutionary programming (EP)
developed by Fogel (1966), and traditionally has
used representations that are tailored to the
problem domain.
For example, in real-valued optimization
problems, the individuals within the population
are real-valued vectors.
Other representations such as ordered lists, and
graphical representations could be applied
depending on the problem itself.
13. Evolutionary programming (EP)
procedure EP
{
t = 0;
initialize population P(t);
evaluate P(t);
until (done) {
t = t + 1;
parent_selection P(t);
mutate P(t);
evaluate P(t);
survive P(t);
}
}
14. Evolutionary programming (EP)
After initialization, all N individuals are selected to be
parents, and then are mutated, producing N children.
These children are evaluated and N survivors are
chosen from the 2N individuals, using a probabilistic
function based on fitness.
In other words, individuals with a greater fitness have a
higher chance of survival.
The form of mutation is based on the representation
used.
15. Evolutionary programming (EP)
For example, when using a real-valued vector,
each variable within an individual may have an
adaptive mutation rate that is normally
distributed with a zero expectation.
Recombination is not generally performed since
the forms of mutation used are quite flexible and
can produce perturbations similar to
recombination, if desired.
16. Evolution strategies (ES)
were independently developed by Rechenberg,
with selection, mutation, and a population of size
one.
Schwefel introduced recombination and
populations with more than one individual, and
provided a nice comparison of ESs with more
traditional optimization techniques.
Evolution strategies typically use real-valued
vector representations.
17. Evolution strategies (ES)
procedure ES; {
t = 0;
initialize population P(t);
evaluate P(t);
until (done) {
t = t + 1;
parent_selection P(t);
recombine P(t)
mutate P(t);
evaluate P(t);
survive P(t);
}
}
18. Evolution strategies (ES)
After initialization and evaluation, individuals are
selected uniformly Randomly to be parents.
In the standard recombinative ES, pairs of parents
produces children via recombination, which are further
perturbed via mutation.
The number of children created is greater than N.
Survival is deterministic and is implemented in one of
two ways:
The first allows the N best children to survive, and replaces the
parents with these children.
The second allows the N best children and parents to survive.
19. Evolution strategies (ES)
Like EP, considerable effort has focused on
adapting mutation as the algorithm runs by
allowing each variable within an individual to
have an adaptive mutation rate that is normally
distributed with a zero expectation.
Unlike EP, however, recombination does play an
important role in evolution strategies, especially
in adapting mutation.
20. Genetic algorithms (GA)
developed by Holland (1975), have traditionally
used a more domain independent
representation, namely, bit-strings.
However, many recent applications of GAs have
focused on other representations, such as
graphs (neural networks), Lisp expressions,
ordered lists, and real-valued vectors.
21. Genetic algorithms (GA)
procedure GA {
t = 0;
initialize population P(t);
evaluate P(t);
until (done) {
t = t + 1;
parent_selection P(t);
recombine P(t)
mutate P(t);
evaluate P(t);
survive P(t);
}
}
22. Genetic algorithms (GA)
After initialization parents are selected according to a
probabilistic function based on relative fitness.
In other words, those individuals with higher relative
fitness are more likely to be selected as parents.
N children are created via recombination from the N
parents.
The N children are mutated and survive, replacing the N
parents in the population.
It is interesting to note that the relative emphasis on
mutation and crossover is opposite to that in EP.
23. Genetic algorithms (GA)
In a GA, mutation flips bits with some small
probability, and is often considered to be a
background operator.
Recombination, on the other hand, is
emphasized as the primary search operator.
GAs are often used as optimizers, although
some researchers emphasize its general
adaptive capabilities (De Jong, 1992).
24. Variations on EP, ES, and GA Themes
These three approaches (EP, ES, and GA)
have served to inspire an increasing
amount of research on and development
of new forms of evolutionary algorithms for
use in specific problem solving contexts.
25. Variations on EP, ES, and GA Themes
One of the most active areas of application
of evolutionary algorithms is in solving
complex function and combinatorial
optimization problems.
A variety of features are typically added to
EAs in this context to improve both the
speed and the precision of the results.
26. Variations on EP, ES, and GA Themes
A second active area of application of EAs
is in the design of robust rule learning
systems.
Holland's (1986) classifier systems were
some of the early examples.
27. Variations on EP, ES, and GA Themes
More recent examples include the SAMUEL
system developed by Grefenstette (1989), the
GABIL system of De Jong and Spears (1991),
and the GIL system of Janikow (1991).
In each case, significant adaptations to the basic
EAs have been made in order to effectively
represent, evaluate, and evolve appropriate rule
sets as defined by the environment.
28. Variations on EP, ES, and GA Themes
One of the most fascinating recent
developments is the use of EAs to evolve more
complex structures such as neural networks and
Lisp code.
This has been dubbed "genetic programming",
and is exemplified by the work of de Garis
(1990), Fujiko and Dickinson (1987), and
Koza (1991).
de Garis evolves weights in neural networks, in
an attempt to build complex behavior.
29. Variations on EP, ES, and GA Themes
Fujiko and Dickinson evolved Lisp expressions
to solve other problems.
Koza also represents individuals using Lisp
expressions and has solved a large number of
optimization and machine learning tasks.
One of the open questions here is precisely what
changes to EAs need to be made in order to
efficiently evolve such complex structures.
30. Representation
Of course, any genetic operator such as mutation and
recombination must be defined with a particular
individual representation in mind.
Again, the EA community differs widely in the
representations used.
Traditionally, GAs use bit strings. In theory, this
representation makes the GA more problem
independent, because once a bit string representation is
found, standard bit-level mutation and recombination can
often be used.
We can also see this as a more genotypic level of
representation, since the individual is in some sense
encoded in the bit string.
31. Representation
However, the GA community has investigated
more distinct representations, including vectors
of real values (Davis, 1989), ordered lists
(Whitley et al., 1989), neural networks (Harp et.
al, 1991), and Lisp expressions (Koza, 1991).
For each of these representations, special
mutation and recombination operators are
introduced.
32. Representation
The EP and ES communities are similar in
this regard.
The ES and EP communities focus on
real-valued vector representations,
although the EP community has also used
ordered list and finite state automata
representations, as suggested by the
domain of the problem.
33. Representation
Although much has been done experimentally,
very little has been said theoretically that helps
one choose good representations, nor that
explains what it means to have a good
representation.
Messy GAs, DPE, and Delta coding all attempt
to manipulate the granularity of the
representation, thus focusing search at the
appropriate level.
Despite some initial success in this area, it is
clear that much more work needs to be done.
34. Adaptive EA
Despite some work on adapting representation,
mutation, and recombination within evolutionary
algorithms, very little has been accomplished with
respect to the adaptation of population sizes and
selection mechanisms.
One way to characterize selection is by the strength of
the selection mechanism.
Strong selection refers to a selection mechanism that
concentrates quickly on the best individuals, while
weaker selection mechanisms allow poor individuals to
survive (and produce children) for a longer period of
time.
35. Adaptive EA
Similarly, the population can be thought of as
having a certain carrying capacity, which refers
to the amount of information that the population
can usefully maintain.
A small population has less carrying capacity,
which is usually adequate for simple problems.
Larger populations, with larger carrying
capacities, are often better for more difficult
problems.
36. Performance Measures, EA-Hardness,
and Evolvability
Of course, one can not refer to adaptation
without having a performance goal in
mind.
EA usually have optimization for a goal.
In other words, they are typically most
interested in finding the best solution as
quickly as possible.
37. Performance Measures, EA-Hardness,
and Evolvability
There is very little theory indicating how
well EAs will perform optimization tasks.
Instead, theory concentrates on what is
referred to as accumulated payoff.
38. Performance Measures, EA-Hardness,
and Evolvability
The difference can be illustrated by considering financial
investment planning over a period of time (stock market).
Instead of trying to find the best stock, you are trying to
maximize your returns as the various stocks are
sampled.
Clearly the two goals are somewhat different, and
maximizing the return may or may not also be a good
heuristic for finding the best stock.
This difference in emphasis has implications in how an
EA practitioner measures performance, which leads to
further implications for how adaptation is accomplished.
39. Performance Measures, EA-Hardness,
and Evolvability
This difference also colors much of the
discussion concerning the issue of problem
difficulty.
The GA community refers to hard problems as
GA-Hard.
Since we are now in the broader context of EAs,
let us refer to hard problems as EA-Hard.
Often, a problem is considered difficult if the EA
can not find the optimum.
40. Performance Measures, EA-Hardness,
and Evolvability
Although this is a quite reasonable definition,
difficult problems are often constructed by taking
advantage of the EA in such a way that selection
deliberately leads the search away from the
optimum.
Such problems are called deceptive.
From a function optimization point of view, the
problem is indeed deceptive, however, the EA
may maximize accumulated payoff.
41. Performance Measures, EA-Hardness,
and Evolvability
Another issue is also very related to a concern of
De Garis, which he refers to as evolvability.
De Garis notes that often his systems do not
evolve at all, namely, that fitness does not
increase over time.
The reasons for this are not clear and remain an
important research topic.
42. Distributed EA
Recent work has concentrated on the
implementation of EAs on parallel machines.
Typically either one processor holds one
individual (in SIMD machines), or a
subpopulation (in MIMD machines).
Clearly, such implementations hold promise of
execution time decreases.
43. Summary
Genetic algorithm - This is the most popular type of EA.
One seeks the solution of a problem in the form of
strings of numbers (traditionally binary, although the best
representations are usually those that reflect something
about the problem being solved - these are not normally
binary), virtually always applying recombination
operators in addition to selection and mutation.
This type of EA is often used in optimization problems.
It is very important to note, however, that while evolution
can be considered to approach an optimum in computer
science terms, actual biological evolution does not seek
an optimum.
44. Summary
Evolutionary programming - Like genetic programming,
only the structure of the program is fixed and its
numerical parameters are allowed to evolve, and Its
main variation operator is mutation.
Evolution strategy - Works with vectors of real numbers
as representations of solutions, and typically uses self-
adaptive mutation rates, as well as recombination.
Genetic programming - Here the solutions are in the
form of computer programs, and their fitness is
determined by their ability to solve a computational
problem.