Results 1 - 10 of 72,856

Integrated-adaptive genetic algorithms

by Henri Luchian, Ru Ioan Cuza Ia¸si, Ovidiu Gheorghies - In ECAL , 2003
"... The aim of this paper is to show that exploiting knowledge extracted from the optimization process is important for the success of an evolutionary solver. In the context of NK fitness landscapes, we identify two causes for the difficulty of an optimization problem: the intrinsic combinatorial diffic ..."
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difficulty and the random-search hybridization. We apply these concepts for the royal road fitness landscape. Experimental results indicate that Integrated-Adaptive Genetic Algorithms (IAGA) are particularly suited for tackling randomsearch hybridization. A learn-as-you-go system aimed at a fine

real-coded adaptive genetic algorithms

by Maxinder S Kanwal, Avinash S Ramesh, Lauren A Huang , 2013
"... A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms [v2; ref status: indexed, ..."
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A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms [v2; ref status: indexed,

Parallel Adaptive Genetic Algorithm

by Leo Budin, Marin Golub, Domagoj Jakobovic - International ICSC/IFAC Symposium on Neural Computation NC’98 , 1998
"... In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is extended with parallelization on one hand and an adaptive operators method on the other. The parallelization of the algorithm ..."
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In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is extended with parallelization on one hand and an adaptive operators method on the other. The parallelization

Co-Adaptive Genetic Algorithms:

by An Example In, Robert E. Smith, Brian Gray
"... This paper focuses on co-adaptive GAs, where population members are interdependent, and adaptation depends on the evolving population context. Recent research on co-adaptive GAs is reviewed. As an example of co-adaptation, a system for Othello strategy acquisition is presented. Preliminary results ..."
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This paper focuses on co-adaptive GAs, where population members are interdependent, and adaptation depends on the evolving population context. Recent research on co-adaptive GAs is reviewed. As an example of co-adaptation, a system for Othello strategy acquisition is presented. Preliminary

The Adaptive Genetic Algorithms for Portfolio Selection Problem

by Wei-guo Zhang, Wei Chen, Ying-luo Wang , 2005
"... Genetic algorithms (GA) are stochastic search techniques based on the mechanics of natural selection and natural genetics. In this paper, the adaptive genetic algorithms are applied to solve the portfolio selection problem in which there exist both probability constraint on the lowest return rate of ..."
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Genetic algorithms (GA) are stochastic search techniques based on the mechanics of natural selection and natural genetics. In this paper, the adaptive genetic algorithms are applied to solve the portfolio selection problem in which there exist both probability constraint on the lowest return rate

Adaptive Genetic Algorithms Based on Fuzzy Techniques

by F. Herrera, M. Lozano - In Proc. of IPMU'96 , 1996
"... The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algor ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic

Self-Adaptive Genetic Algorithm for Clustering

by Juha Kivijärvi, Pasi Fränti, Olli Nevalainen , 2003
"... Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We intro ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We

Adaptive Genetic Algorithms Based on Coevolution with Fuzzy Behaviors

by F. Herrera, M. Lozano - DEPT. OF COMPUTER SCIENCE AND A. I., UNIVERSITY OF GRANADA, SPAIN. (AVAILABLE , 1998
"... Adaptive genetic algorithms dynamically adjust the genetic algorithm configuration during the course of evolving a problem solution in order to offer an appropriate balance between exploration (overall search in the solution space) and exploitation (localized search in the promising regions discover ..."
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Adaptive genetic algorithms dynamically adjust the genetic algorithm configuration during the course of evolving a problem solution in order to offer an appropriate balance between exploration (overall search in the solution space) and exploitation (localized search in the promising regions

Graph Coloring with Adaptive Genetic Algorithms

by A. E. Eiben, J. K. Van Der Hauw , 1996
"... This technical report summarizes our results on solving graph coloring problems with Genetic Algorithms (GA). After testing many different options we conclude that the best one is a (1+1) order-based GA using an adaptation mechanism that periodically changes the fitness function, thus guiding the GA ..."
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This technical report summarizes our results on solving graph coloring problems with Genetic Algorithms (GA). After testing many different options we conclude that the best one is a (1+1) order-based GA using an adaptation mechanism that periodically changes the fitness function, thus guiding

Triangulation of Bayesian Networks Using an Adaptive Genetic Algorithm

by Hao Wang , Kui Yu , Xindong Wu , Hongliang Yao , 2006
"... Abstract. The search for an optimal node elimination sequence for the triangulation of Bayesian networks is an NP-hard problem. In this paper, a new method, called the TAGA algorithm, is proposed to search for the optimal node elimination sequence. TAGA adjusts the probabilities of crossover and mu ..."
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-line and off-line performances. Experimental results show that the TAGA algorithm outperforms a simple genetic algorithm, an existing adaptive genetic algorithm, and simulated annealing on three Bayesian networks.
Results 1 - 10 of 72,856