Results 181 to 190 of about 347,065 (259)

SCHEMA ANALYSIS OF GENETIC ALGORITHMS ON MULTIPLICATIVE LANDSCAPE

Asia-Pacific Conference on Simulated Evolution and Learning, 2004
Hiroshi Furutani
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A Simpler Derivation of Schema Hazard in Genetic Algorithms

Information Processing Letters, 1995
In a previous paper we derived an epression for the schema hazard function at any particular generation in the genetic algorithm. A much simpler derivation is presented here. It is more direct and eliminates the need for the use of the multinomial distribution and the multiple Poisson distribution.
U. Chakraborty
semanticscholar   +3 more sources

Schema theorem of real-coded nonlinear genetic algorithm

Proceedings. International Conference on Machine Learning and Cybernetics, 2003
Through the mechanism analysis of simple genetic algorithm (SGA), every genetic operator can be considered as a linear function. So some disadvantages of SGA may be solved if the genetic operators are modified to a nonlinear function. According to the above method, a nonlinear genetic algorithm is introduced.
Zhihua Cui, Jianchao Zeng
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Parallel Genetic Algorithms with Schema Migration

Proceedings 26th Annual International Computer Software and Applications, 2003
Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs.
Guan Yu
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Mathematical analysis of schema survival for genetic algorithms having dual mutation

Soft Computing, 2017
Genetic algorithms are widely used in the field of optimization. Schema theory forms the foundational basis for the success of genetic algorithms. Traditional genetic algorithms involve only a single mutation phase per iteration of the algorithm. In this paper, a novel concept of genetic algorithms involving two mutation steps per iteration is proposed.
Apoorva Mishra, A. Shukla
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Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover

Genetic Programming and Evolvable Machines, 2001
Summary: A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value.
R. Poli
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Genetic algorithm based on schema comparison

Journal of Computer Applications, 2008
Jin-rong XU
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The circular schema theorem for genetic algorithms and two-point crossover

Second International Conference on Genetic Algorithms in Engineering Systems, 1997
The schema theorem is the classical formulation of the search strategy performed by genetic algorithms (adaptation procedures mimicking biological evolution and molecular genetics). The original schema theorem has been derived for single-point crossover assuming that the individual chromosomes are arranged as strings.
A. Neubauer
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Genetic Algorithms (GAs) and Their Mathematical Foundations

Advances in Computational Intelligence and Robotics, 2021
In this chapter, the authors back GA procedures using old mathematical facts. More rigorous working of mathematical facts about GAs are raised in this chapter. In fact, there are a large number of similarities in the population of strings.

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Short Notes on the Schema Theorem and the Building Block Hypothesis in Genetic Algorithms

Evolutionary Programming, 1998
After decades of success, research on evolutionary algorithms aims at developing a sound theory that describes and predict the behavior of these algorithms. One research topic of interest is the analysis of the role of crossover and recombination in genetic algorithms, especially since various papers come to different conclusions.
R. Salomon
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