Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
A preliminary investigation of evolution as a form design strategy (poster)
ALIFE Proceedings of the sixth international conference on Artificial life
What can we do about the unnecessary diversity of notation for syntactic definitions?
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stalk: An Interactive System for Virtual Molecular Docking
IEEE Computational Science & Engineering
Grammatical Retina Description with Enhanced Methods
Proceedings of the European Conference on Genetic Programming
An Evolutionary Optimum Searching Tool
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Classification using cultural co-evolution and genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Exposing parameters of a trained dynamic model for interactive music creation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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The purpose of Generic Evolutionary Algorithms Programming Library (GEA1) system is to provide researchers with an easy-to-use, widely applicable and extendable programming library which solves real-world optimization problems by means of evolutionary algorithms. It contains algorithms for various evolutionary methods, implemented genetic operators for the most common representation forms for individuals, various selection methods, and examples on how to use and expand the library. All these functions assure that GEA can be effectively applied on many problems. GraphGEA is a graphical user interface to GEA written with the GTK API. The numerous parameters of the evolutionary algorithm can be set in appropriate dialog boxes. The program also checks the correctness of the parameters and saving/restoring of parameter sets is also possible. The selected evolutionary algorithm can be executed interactively on the specified optimization problem through the graphical user interface of GraphGEA, and the results and behavior of the EA can be observed on several selected graphs and drawings. While the main purpose of GEA is solving optimization problems, that of GraphGEA is education and analysis. It can be of great help for students understanding the characteristics of evolutionary algorithms and researchers of the area can use it to analyze an EA's behavior on particular problems.