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170
Multiobjective Evolutionary Algorithms: Analyzing the StateoftheArt
, 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
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Cited by 424 (7 self)
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Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of c...
Fast evolutionary programming
 Proceeding on Fifth Annual Conference on Evolutionary Programming
, 1996
"... AbstmctThis paper presents a study of parallel evolutionary programming (EP). The paper is divided into two parts. The first part proposes a concept of parallel EP. Four numerical fmctions are used to compare the performance between the serial algorithm and the parallel algorithm. In the second par ..."
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Cited by 49 (4 self)
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AbstmctThis paper presents a study of parallel evolutionary programming (EP). The paper is divided into two parts. The first part proposes a concept of parallel EP. Four numerical fmctions are used to compare the performance between the serial algorithm and the parallel algorithm. In the second part, we apply parallel Ep to a more complicated problem an evolving neural networks pmhlem. The results from this problem show that the parallel version h not only faster than the serial version, but the parallel version also more reliably finds optimal solutions. I.
Efficient Parallel Genetic Algorithms: Theory and Practice
 Computer Methods in Applied Mechanics and Engineering
, 2000
"... Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, becaus ..."
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Cited by 47 (1 self)
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Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which affect their search quality and their efficiency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, because previous studies show that there is a crucial relation between solution quality and population size. As a first step, the paper shows how to size a simple GA to reach a solution of a desired quality. The simple GA is then parallelized, and its execution time is optimized. The rest of the paper deals with parallel GAs with multiple populations. Two bounding cases of the migration rate and topology are analyzed, and the case that yields good speedups is optimized. Later, the models are specialized to consider sparse topologies and migration rates that are more likely to be used by practitioners. The paper also presents the additional advantages of combining multi and singlepopulation parallel GAs. The results of this work are simple models that practitioners may use to design efficient and competent parallel GAs.
Parallelization Strategies for Ant Colony Optimization
 Proceedings of PPSNV, Fifth International Conference on Parallel Problem Solving from Nature
, 1998
"... . Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied to NPhard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, th ..."
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Cited by 38 (6 self)
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. Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied to NPhard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applying MAX MIN Ant System, one of the most efficient ACO algorithms, to the Traveling Salesman Problem and show that using parallel independent runs is very effective. 1 Introduction Ant Colony Optimization (ACO) is a new population based search metaphor inspired by the foraging behavior of real ants. Among the basic ideas underlying ACO is to use an algorithmic counterpart to the pheromone trail, used by real ants, as a medium for communication among a colony of artificial ants. The seminal work on ACO is Ant System [9, 11] which was first proposed for solving the Traveling Salesman Problem (TS...
Designing Efficient MasterSlave Parallel Genetic Algorithms
, 1997
"... A simple technique to reduce the execution time of genetic algorithms (GAs) is to divide the task of evaluating the population among several processors. This class of algorithms is called "global" parallel GAs because selection and mating consider the entire population. Global parallel GAs ..."
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Cited by 30 (4 self)
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A simple technique to reduce the execution time of genetic algorithms (GAs) is to divide the task of evaluating the population among several processors. This class of algorithms is called "global" parallel GAs because selection and mating consider the entire population. Global parallel GAs are usually implemented as masterslave programs and require constant interprocessor communication. This will affect their performance, but most investigations of these algorithms ignore the penalty caused by communications. This paper presents an analysis of the execution time of global parallel GAs that includes a simple model of the time used in communications and shows that there is an optimal number of processors that minimizes the execution time. To further reduce the execution time we recommend the use of hybrids that combine global and coarsegrained parallel GAs. 1 Introduction A simple technique to parallelize genetic algorithms is to divide the task of evaluating the population among seve...
Human Based Genetic Algorithm
 IEEE Transactions on Systems, Man, and Cybernetics
, 2001
"... In this paper, a new class of genetic algorithms (GA) is presented. It is based on the idea ..."
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Cited by 29 (0 self)
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In this paper, a new class of genetic algorithms (GA) is presented. It is based on the idea
Using Evolutionary Algorithms to Induce Oblique Decision Trees
 In
, 2000
"... This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that at least in some cases this ..."
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Cited by 16 (4 self)
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This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that at least in some cases this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolution strategy and a simple genetic algorithm on public domain and artificial data sets. The empirical results suggest that the EAs quickly find competitive classifiers, and that EAs scale up better than traditional methods to the dimensionality of the domain and the number of instances used in training.
Possibilities and Limitations of Applying Evolvable Hardware to RealWorld Applications
 in FieldProgrammable Logic and Applications: 10th International Conference on Field Programmable Logic and Applications (FPL2000), R.W. Hartenstein et al., Eds
, 2000
"... Evolvable Hardware (EHW) has been proposed as a new method for designing systems for realworld applications. This paper contains a classification of the published work on this topic. Further, a thorough discussion about the limitations of the present EHW and possible solutions to these are proposed ..."
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Cited by 15 (4 self)
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Evolvable Hardware (EHW) has been proposed as a new method for designing systems for realworld applications. This paper contains a classification of the published work on this topic. Further, a thorough discussion about the limitations of the present EHW and possible solutions to these are proposed. EHW has been applied to a wide range of applications. However, to solve more complex applications, the evolutionary schemes should be improved.
Simulating Evolution with a Computational Model of Embryogeny
, 2006
"... Natural evolution is an incredibly complex dynamical system which we are still grasping to understand. One could argue that the evolutionary search process itself is not the most important part of evolution, but rather it is the evolved representational structures and physical implementations of the ..."
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Cited by 15 (1 self)
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Natural evolution is an incredibly complex dynamical system which we are still grasping to understand. One could argue that the evolutionary search process itself is not the most important part of evolution, but rather it is the evolved representational structures and physical implementations of the genotypephenotype mappings that make it so powerful. One such behaviour is embryogeny, the process by which genetic representations within cells control the development of a multicellular organism. Evolution of complex organisms is highly dependent upon biological mechanisms such as embryogeny, and this has fundamental consequences. The objective of this work is to investigate these consequences for a simulated evolutionary search process using a computational model of embryogeny. The model simulates the dynamics achieved within biological systems between the genetic representation of an individual and the mapping to its phenotypic representation. It is not an attempt to produce a biologically accurate or plausible model of the cell, growth or genetics. The model is used to create individuals which develop into multicomponent patterns