Results 1  10
of
15
A ParameterLess Genetic Algorithm
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1999
"... From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, a ..."
Abstract

Cited by 230 (33 self)
 Add to MetaCart
From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to handin the problem to a blackbox algorithm, and simply press a start button. This paper explores the development of a GA that fulfills this requirement. It has no parameters whatsoever. The development of the algorithm takes into account several aspects of the theory of GAs, including previous research work on population sizing, the schema theorem, building block mixing, and genetic drift.
Niching Methods for Genetic Algorithms
, 1995
"... Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This ..."
Abstract

Cited by 192 (1 self)
 Add to MetaCart
Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods. To achieve this purpose, it first develops a general framework for the modelling of niching methods, and then applies this framework to construct models of individual niching methods, specifically crowding and sharing methods. Using a constructed model of crowding, this study determines why crowding methods over the last two decades have not made effective niching methods. A series of tests and design modifications results in the development of a highly effective form of crowding, called determin...
Finite Markov Chain Analysis of Genetic Algorithms with Niching
 Proceedings of the Fifth International Conference on Genetic Algorithms
, 1993
"... Finite, discretetime Markov chain models of genetic algorithms have been used successfully in the past to understand the complex dynamics of a simple GA. Markov chains can exactly model the GA by accounting for all of the stochasticity introduced by various GA operators, such as initialization, sel ..."
Abstract

Cited by 31 (7 self)
 Add to MetaCart
Finite, discretetime Markov chain models of genetic algorithms have been used successfully in the past to understand the complex dynamics of a simple GA. Markov chains can exactly model the GA by accounting for all of the stochasticity introduced by various GA operators, such as initialization, selection, crossover, and mutation. Although such models quickly become unwieldy with increasing population size or genome length, they provide initial insights that guide our development of approximate, scalable models. In this study, we use Markov chains to analyze the stochastic effects of the "niching operator" of a niched GA. Specifically, we model the effect of fitness sharing on a singlelocus genome. Without niching, our model is an absorbing Markov chain. With niching, we are dealing with a "quasiergodic" Markov chain. Rather than calculating expected times to absorption, we are interested in steadystate probabilities for positive recurrent states. Established techniques for analyzin...
Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization
 Urbana: Department of General Engineering, University of Illinois at UrbanaChampaign
, 1993
"... This paper presents simple analytical models of genetic algorithms which are commonly used in multimodal function optimization. The methodology for constructing the models is similar throughout the study. The predictive value of each model is verified by running the corresponding genetic algorithm o ..."
Abstract

Cited by 21 (0 self)
 Add to MetaCart
This paper presents simple analytical models of genetic algorithms which are commonly used in multimodal function optimization. The methodology for constructing the models is similar throughout the study. The predictive value of each model is verified by running the corresponding genetic algorithm on various multimodal functions of varying complexity.
Adaptive Niching via Coevolutionary Sharing
 In Genetic Algorithms and Evolution Strategy in Engineering and Computer Science (Chapter 2
, 1997
"... An adaptive niching scheme called coevolutionary shared niching (CSN) is proposed, implemented, analyzed and tested. The scheme overcomes the limitations of fixed sharing schemes by permitting the locations and radii of niches to adapt to complex landscapes, thereby permitting a better distribution ..."
Abstract

Cited by 17 (4 self)
 Add to MetaCart
An adaptive niching scheme called coevolutionary shared niching (CSN) is proposed, implemented, analyzed and tested. The scheme overcomes the limitations of fixed sharing schemes by permitting the locations and radii of niches to adapt to complex landscapes, thereby permitting a better distribution of solutions in problems with many badly spaced optima. The scheme takes its inspiration from the model of monopolistic competition in economics and utilizes two populations, a population of businessmen and a population of customers, where the locations of the businessmen correspond to niche locations and the locations of customers correspond to solutions. Initial results on straightforward test functions validate the distributional effectiveness of the basic scheme, although tests on a massively multimodal function do not find the best niches in the allotted time. This result spurs the design of an imprint mechanism that turns the best customers into businessmen, thereby making better use o...
The ParameterLess Genetic Algorithm: Rational And Automated Parameter Selection For Simplified Genetic Algorithm Operation
, 2000
"... Genetic algorithms (GAs) have been used to solve difficult optimization problems in a number of fields. One of the advantages of these algorithms is that they operate well even in domains where little is known, thus giving the GA the flavor of a general purpose problem solver. However, in order ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
Genetic algorithms (GAs) have been used to solve difficult optimization problems in a number of fields. One of the advantages of these algorithms is that they operate well even in domains where little is known, thus giving the GA the flavor of a general purpose problem solver. However, in order to solve a problem with the GA, the user usually has to specify a number of parameters that have little to do with the user's problem, and have more to do with the way the GA operates. This dissertation presents a technique that greatly simplifies the GA operation by relieving the user from having to set these parameters. Instead, the parameters are set automatically by the algorithm itself. The validity of the approach is illustrated with artificial problems often used to test GA techniques, and also with a simplified version of a network expansion problem.
Genetic Algorithm in Search and Optimization: The Technique and Applications
 Proc. of Int. Workshop on Soft Computing and Intelligent Systems
, 1997
"... A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a fitness which ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operatorsreproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch ...
A ParameterLess Genetic Algorithm
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1999
"... From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and wha ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to handin the problem to a blackbox algorithm, and simply press a start button. This paper explores the development of a GA that fulfills this requirement. It has no parameters whatsoever. The development of the algorithm takes into account several aspects of the theory of GAs, including previous research works on population sizing, the schema theorem, building block mixing, and genetic drift.
GASelection Revisited from an ESDriven Point of View
"... Abstract. Whereas the selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realized by the selection of aboveaverage parents for reproduction, Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
Abstract. Whereas the selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realized by the selection of aboveaverage parents for reproduction, Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest due to birth surplus). This contribution proposes a generic and enhanced selection model for GAs considering selection aspects of population genetics and ES. Some selected aspects of these enhanced techniques are discussed exemplarily on the basis of standardized benchmark problems. 1
An Analysis of a Reordering Operator with Tournament Selectionon a GAHard Problem
 Proceedings of Genetic and Evolutionary Computation Conference 2003 (GECCO2003
, 2003
"... This paper analyzes the performance of a genetic algorithm that utilizes tournament selection, onepoint crossover, and a reordering operator. A model is proposed to describe the combined effect of the reordering operator and tournament selection, and the numerical solutions are presented as well. P ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
This paper analyzes the performance of a genetic algorithm that utilizes tournament selection, onepoint crossover, and a reordering operator. A model is proposed to describe the combined effect of the reordering operator and tournament selection, and the numerical solutions are presented as well. Pairwise, sary, and probabilistic tournament selection are all included in the proposed model. It is also demonstrated that the upper bound of the probability to apply the reordering operator, previously derived with proportionate selection, does not affect the performance. Therefore, tournament selection is a necessity when using a reordering operator in a genetic algorithm.