Summary
The present work proposes the architecture Clonart (Clonal Adaptive Resonance Theory), a Hybrid Model that employs techniques like intelligent operators, clonal selection principle, local search, memory antibodies and ART clusterization, in order to increase the performance of the algorithm. The approach uses a mechanism similar to the ART 1 network for storing a population of memory antibodies that will be responsible for the acquired knowledge of the algorithm. This characteristic allows the algorithm a self-organization of the antibodies in accordance with the complexity of the database.
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Extract
Artificial Immune System with Art Memory Hibridization
1. Introduction
There are a number of factors that motivate the hybridization of evolutionary algorithms with other techniques. Many complex problems can be decomposed into a number of parts, for some of which exact methods, or very good heuristics, may already be available. In these cases it makes sense to use a combination of the most appropriate methods for different subproblems.An overall successful and efficient general problem solver, in fact, do not exist. It is commonly experienced that the combination of an evolutionary algorithm and a heuristic method (the hybrid EA) performs better than either of the "parents" algorithms alone [H].For this reason, a Memetic Algorithm [9] was developed based on features of natural immune systems. Memetic algorithms are characterized by the hybridization of Evolutionary Algorithms with the use of Local Searches at particular points of the algorithm [3], [4] and [9].Section 2 gives an introduction about the adaptive resonance theory. section 3 de...See the full content of this document
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