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Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization
 IEEE International Conference on Evolutionary Computation
, 1996
"... Genetic algorithms utilize populations of individual hypotheses that converge over time to a single optimum, even within a multimodal domain. This paper examines methods that enable genetic algorithms to identify multiple optima within multimodal domains by maintaining population members within the ..."
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Cited by 43 (0 self)
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Genetic algorithms utilize populations of individual hypotheses that converge over time to a single optimum, even within a multimodal domain. This paper examines methods that enable genetic algorithms to identify multiple optima within multimodal domains by maintaining population members within the niches defined by the multiple optima. A new mechanism, Dynamic Niche Sharing, is developed that is able to efficiently identify and search multiple niches (peaks) in a multimodal domain. Dynamic niche sharing is shown to perform better than two other methods for multiple optima identification, Standard Sharing and Deterministic Crowding. To further improve performance, mating restrictions are used to increase the likelihood of producing highly fit offspring. Two new mating restriction mechanisms, Dynamic Line Breeding and Dynamic Inbreeding, are introduced that utilize dynamic niche sharing to proportionately populate local optima in a multimodal domain. Experiments presented in this paper ...
Hierarchical Problem Solving by the Bayesian Optimization Algorithm
 PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE 2000
, 2000
"... The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimi ..."
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Cited by 29 (8 self)
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The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimization algorithm (BOA) is extended in order to solve the proposed class of problems.
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
, 1997
"... ..."
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 ..."
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Cited by 18 (4 self)
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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...
Multidisciplinary Shape Optimization in Aerodynamics and Electromagnetics using Genetic Algorithms
, 1998
"... this paper, an approximation for the Pareto set of optimal solutions is obtained by using a genetic algorithm (GA). The first objective function is the drag coefficient. As a constraint, it is required that the lift coefficient is above a given value. The CFD analysis solver is based on the finite v ..."
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Cited by 11 (5 self)
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this paper, an approximation for the Pareto set of optimal solutions is obtained by using a genetic algorithm (GA). The first objective function is the drag coefficient. As a constraint, it is required that the lift coefficient is above a given value. The CFD analysis solver is based on the finite volume discretization of the inviscid Euler equations. The second objective function is equivalent to the integral of the transverse magnetic radar cross section (RCS) over a given sector. The computational electromagnetics (CEM) wave field analysis requires the solution of a twodimensional Helmholtz equation which is obtained using a fictitious domain method. Numerical experiments illustrate the above evolutionary methodology on an IBM SP2 parallel computer. c fl ??? John Wiley & Sons, Inc.
Genetic Drift in Sharing Methods
 in Proceedings of the First IEEE Conference on Evolutionary Computation
, 1994
"... Adding a sharing method to a genetic algorithm promotes the formation and maintenance of stable subpopulations. This paper explores the limits of sharing by deriving closedform expressions for the expected time to disappearance of a subpopulation. The time to disappearance is shown to be an exponen ..."
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Cited by 11 (1 self)
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Adding a sharing method to a genetic algorithm promotes the formation and maintenance of stable subpopulations. This paper explores the limits of sharing by deriving closedform expressions for the expected time to disappearance of a subpopulation. The time to disappearance is shown to be an exponential function of population size, with relative subpopulation fitnesses determining the base of the exponential. However, disappearance time decreases rapidly as the number of subpopulations increases. Both theoretical and experimental illustrations are given of genetic drift in sharing. I. Introduction Given a problem with multiple solutions, the traditional genetic algorithm (GA) will at best, ultimately converge to a population containing only one of those solutions [1]. The culprit, known as genetic drift, can be defined as the convergence of a finite population in the absence of selection pressure, due to variance in the selection process. To combat genetic drift, GAs were developed t...
From twomax to the Ising model: Easy and hard symmetrical problems
 In Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2002
, 2002
"... The paper shows that there is a key dividing line between two types of symmetrical problems: problems for which a genetic algorithm (GA) benefits from the fact that genetic drift chooses between equally good partial solutions, and problems for which all equally good partial solutions have to be pres ..."
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Cited by 11 (0 self)
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The paper shows that there is a key dividing line between two types of symmetrical problems: problems for which a genetic algorithm (GA) benefits from the fact that genetic drift chooses between equally good partial solutions, and problems for which all equally good partial solutions have to be preserved to find an optimum. By analyzing in detail the search process of a selectorecombinative GA optimizing a TwoMax and comparing this search process with that of a onedimensional Ising model, the paper investigates the difference between these two types of symmetrical problems. For the first type of problems, naively adding a niching technique to the genetic algorithm makes the problem harder to solve. For the last type of problems, niching is necessary to find an optimum. 1
Bounding the Effect of Noise in Multiobjective Learning Classifier Systems
, 2003
"... This paper analyzes the impact of using noisy data sets in Pittsburghstyle learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with ..."
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Cited by 9 (2 self)
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This paper analyzes the impact of using noisy data sets in Pittsburghstyle learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting the minimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as over fitting conditions that lead to hypotheses that generalize the concept to be learned poorly.
Population Sizing for Sharing Methods
 In Foundations of Genetic Algorithms 3
, 1994
"... Sharing methods promote the formation and maintenance of stable subpopulations or niches in genetic algorithms. This paper derives, for various models of sharing, lower bounds on the population size required to maintain, with probability fl, a fixed number of niches. The first model assumes all nic ..."
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Cited by 8 (0 self)
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Sharing methods promote the formation and maintenance of stable subpopulations or niches in genetic algorithms. This paper derives, for various models of sharing, lower bounds on the population size required to maintain, with probability fl, a fixed number of niches. The first model assumes all niches are equivalent with respect to fitness and that crossover is minimally disruptive. The next two models allow niches to differ with respect to fitness. The final model takes into account the disruptive effects of crossover. GAs with sharing are run on seven test problems in optimization and classification, using population sizes derived from the models. 1 Introduction The simple genetic algorithm (GA), when confronted with a problem having multiple solutions, will converge, in the best case, to a population containing only one of those solutions (Goldberg & Segrest, 1987). This behavior prompted the development of GAs capable of forming and maintaining stable subpopulations or niches. GA...