Evolutionary Neuroestimation of Fitness Functions

Aguilar–Ruiz, Jesús S.; Mateos, Daniel; Rodriguez, D.

doi:10.1007/978-3-540-24580-3_15

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This idea has also been adapted to learning tasks. In this sense, the Neuro-Evolutionary Model (NEM [2]) includes a neural network which is trained to estimate the fitness function during the evolutionary process, so the EA gathers speed in time.…”

Section: ) Evaluationmentioning

confidence: 99%

“…Many evolutionary algorithms for solving multiobjective optimization problems have been developed. The most recent ones are the "-multiobjective evolutionary algorithm ("-MOEA) [5], nondominated sorting genetic algorithm-II (NSGA-II) [3], strength Pareto evolutionary algorithm-II (SPEA-II) [16], and Pareto envelope-based selection-II (PESA-II) [2]. Most of these approaches propose the use of a generational GA.…”

Section: Multiobjective Ga Optimization Using Reduced Modelsmentioning

confidence: 99%

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Knowledge-Based Fast Evaluation for Evolutionary Learning

Giráldez

Aguilar–Ruiz

Riquelme

2005

IEEE Trans. Syst., Man, Cybern. C

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Abstract-The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources, such as evolutionary computation. Efficacy and efficiency are two critical aspects for knowledge-based techniques. The incorporation of knowledge into evolutionary algorithms (EAs) should provide either better solutions (efficacy) or the equivalent solutions in shorter time (efficiency), regarding the same evolutionary algorithm without incorporating such knowledge. In this paper, we categorize and summarize some of the incorporation of knowledge techniques for evolutionary algorithms and present a novel data structure, called efficient evaluation structure (EES), which helps the evolutionary algorithm to provide decision rules using less computational resources. The EES-based EA is tested and compared to another EA system and the experimental results show the quality of our approach, reducing the computational cost about 50%, maintaining the global accuracy of the final set of decision rules.

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Section: ) Evaluationmentioning

confidence: 99%