An intelligent system should be able to solve a wide range of problems from different domains. In this paper we propose a complex and adaptive system capable of solving various data analysis problems without needing h...
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An intelligent system should be able to solve a wide range of problems from different domains. In this paper we propose a complex and adaptive system capable of solving various data analysis problems without needing human help for parameter settings. The system, called A- Brain, consists of several interconnected components ( a decision- maker, a trainer, and several problem solvers) which provide a base for building complex problem solvers. The parameters of the trainer's algorithm are problem independent. This fact is a requirement for intelligent systems which cannot rely on human intervention while operating. The A- Brain system is used to solve some well- known problems in the. field of symbolic regression and classification. Numerical experiments show that the A- Brain system is able to perform very well on the considered test problems.
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 re...
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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.
Intelligence is strongly related to the ability of solving different problems by a single system. General problems solvers such as Artificial Neural Networks, Evolutionary Algorithms, Particle Swarm etc, have traditio...
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ISBN:
(纸本)9781595931085
Intelligence is strongly related to the ability of solving different problems by a single system. General problems solvers such as Artificial Neural Networks, Evolutionary Algorithms, Particle Swarm etc, have traditionally been tested against one problem at one time. The purpose of this research is to build a complex and adaptive system able to solve multiple (and different) problems. The proposed system, called A-Brain, consists of several connected components (a Decision Maker, a Trainer and several Problem Solvers) which provide a base for building complex problem solvers. The A-Brain system is applied for solving some well-known problems in the field of symbolic regression. Numerical experiments show that A-Brain system is able to perform very well on the considered test problems.
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