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evolving evolutionary algorithms with patterns

SOFT COMPUTING 2007年第6期11卷 503-518页

作者： Oltean, Mihai Univ Babes Bolyai Fac Math & Comp Sci Dept Comp Sci R-3400 Cluj Napoca Romania

A new model for evolving evolutionary algorithms (EAs) is proposed in this paper. The model is based on the multi expression programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.

关键词： genetic algorithms genetic programming evolving evolutionary algorithms multi expression programming linear genetic programming

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evolving evolutionary algorithms using linear genetic programming

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evolutionary COMPUTATION 2005年第3期13卷 387-410页

作者： Oltean, M Univ Babes Bolyai Dept Comp Sci R-3400 Cluj Napoca Romania

A new model for evolving evolutionary algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several evolutionary algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved evolutionary algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

关键词： genetic algorithms genetic programming linear genetic programming evolving evolutionary algorithms

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evolutionary design of evolutionary algorithms

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2009年第3期10卷 263-306页

作者： Diosan, Laura Oltean, Mihai Univ Babes Bolyai Fac Math & Comp Sci Dept Comp Sci Cluj Napoca 400084 Romania

Manual design of evolutionary algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several evolutionary algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

关键词： Genetic algorithms evolving evolutionary algorithms Meta genetic programming Function optimization

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