Results 221 to 230 of about 451,148 (267)
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2009 IEEE Congress on Evolutionary Computation, 2009
Genetic algorithms (GAs) are classical evolutionary computation methods, which have a wild application prospect. This paper proposes an improved genetic algorithm, named the isoline genetic algorithm (IGA), for numerical optimization. The proposed algorithm utilizes the population to model isolines of fitness in the search space.
Ying Lin 0001, Jun Zhang 0003
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Genetic algorithms (GAs) are classical evolutionary computation methods, which have a wild application prospect. This paper proposes an improved genetic algorithm, named the isoline genetic algorithm (IGA), for numerical optimization. The proposed algorithm utilizes the population to model isolines of fitness in the search space.
Ying Lin 0001, Jun Zhang 0003
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An alternative Genetic Algorithm
2006This paper presents a new Genetic Algorithm (GA), called Alternative Genetic Algorithm (AGA) which has been defined to facilitate theoretical investigations. We have shown that both AGA and the usual GA (UGA) obey similar difference equations. However, theoretical investigations on the AGA are much simpler than on the UGA. For the AGA, we can derive as
Hesser, Jürgen, Männer, Reinhard
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Evolutionary Computation, 1994
The original schema theorem (an inequality) has been replaced by an equality that determines the expected next generation for a simple genetic algorithm. This has made possible the computation of the trajectory of expected next generations. Visualization of these evolutionary trajectories beginning from different initial populations has led to the ...
Jenny Juliany, Michael D. Vose
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The original schema theorem (an inequality) has been replaced by an equality that determines the expected next generation for a simple genetic algorithm. This has made possible the computation of the trajectory of expected next generations. Visualization of these evolutionary trajectories beginning from different initial populations has led to the ...
Jenny Juliany, Michael D. Vose
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Genetic Algorithms-a Tool for OR?
Journal of the Operational Research Society, 1996Summary: Compared with other metaheuristic techniques such as simulated annealing and tabu search, research into the use of genetic algorithms for the solution of OR problems is still in its infancy. This paper provides an introduction to genetic algorithms and their use in the solution of both classical and practical operational research problems ...
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Computer, 1994
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice.
Srinivas, M, Patnaik, LM
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Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice.
Srinivas, M, Patnaik, LM
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Neural Computing and Applications, 2009
Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes.
Qijian Chen +2 more
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Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes.
Qijian Chen +2 more
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Hierarchical Genetic Algorithms
2004Current Genetic Algorithms can efficiently address order-k separable problems, in which the order of the linkage is restricted to a low value k. Outside this class, there exist hierarchical problems that cannot be addressed by current genetic algorithms, yet can be addressed efficiently in principle by exploiting hierarchy.
de Jong, E.D. +2 more
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On coevolutionary genetic algorithms
Soft Computing, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2010 Seventh International Conference on Information Technology: New Generations, 2010
This paper proposes a new genetic algorithm called a meiosis genetic algorithm (MGA) based on meiosis cell division. The meiosis cell division serves producing chromosome diversity which speeds up converging to a mature solution chromosome. The experiment results on a knapsack problem using the new algorithm show that it is more efficient than the ...
Nattavut Wiriyasermkul +2 more
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This paper proposes a new genetic algorithm called a meiosis genetic algorithm (MGA) based on meiosis cell division. The meiosis cell division serves producing chromosome diversity which speeds up converging to a mature solution chromosome. The experiment results on a knapsack problem using the new algorithm show that it is more efficient than the ...
Nattavut Wiriyasermkul +2 more
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Information Management & Computer Security, 2004
Describes one of the newest forms of artificial intelligence being applied to the solution of business problems – the genetic algorithm (GA). GAs are useful when a problem has multiple solutions, some of which are better than others. Unlike deterministic, linear and non‐linear optimization models, GAs test a variety of solutions and, through an ...
Fritz H. Grupe, Simon Jooste
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Describes one of the newest forms of artificial intelligence being applied to the solution of business problems – the genetic algorithm (GA). GAs are useful when a problem has multiple solutions, some of which are better than others. Unlike deterministic, linear and non‐linear optimization models, GAs test a variety of solutions and, through an ...
Fritz H. Grupe, Simon Jooste
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