Java Evolutionary Framework based on Genetic Programming

Java Evolutionary Framework based on Genetic Programming

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Automatic optimization techniques, such as evolutionary algorithms, have become popular in the recent years as a general, simple, robust, and scalable solution which can be applied when other optimization method fails. Recently, many evolutionary and/or genetic based optimization frameworks and libraries have been developed and lot of them is freely available. On the other hand, there are not many tools in optimization field that allows the researchers to implement own code, modify existing code or compare different algorithms. This paper proposes a new grammar driven genetic programming based framework implemented in cross-platform Java programming language which allows to implement own code, modify existing, and analyze algorithms. The framework described in this paper addresses the problem of flexibility, modularity, multiplatformness, and presents a general architecture for evolutionary optimization based on genetic programming driven by context free grammar distributed under the LGPL license suitable for both scientific and business applications. In the paper is described a design of the framework, the motivation for development, and two use-cases.

Automatic optimization techniques, such as evolutionary algorithms, have become popular in the recent years as a general, simple, robust, and scalable solution which can be applied when other optimization method fails. Recently, many evolutionary and/or genetic based optimization frameworks and libraries have been developed and lot of them is freely available. On the other hand, there are not many tools in optimization field that allows the researchers to implement own code, modify existing code or compare different algorithms. This paper proposes a new grammar driven genetic programming based framework implemented in cross-platform Java programming language which allows to implement own code, modify existing, and analyze algorithms. The framework described in this paper addresses the problem of flexibility, modularity, multiplatformness, and presents a general architecture for evolutionary optimization based on genetic programming driven by context free grammar distributed under the LGPL license suitable for both scientific and business applications. In the paper is described a design of the framework, the motivation for development, and two use-cases.

Framework, Genetic Programming, Optimization

Framework, Genetic Programming, Optimization

KARÁSEK, J.; BURGET, R.; DUTTA, M.; SINGH, A.

2017

21.02.2014

New Delhi, Republic of India

978-1-4799-2865-1

2014 International Conference on Signal Processing and Integrated Networks (SPIN)

606

612

7