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The immune and the chemical crossover

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1 Author(s)
Bersini, H. ; IRIDIA Lab., Univ. Libre de Bruxelles, Brussels, Belgium

Among the different mechanisms employed by evolutionary algorithms, it can be argued that recombination, or crossover, is the most original, intuitively appealing and useful in an engineering perspective. It is a simple, but natural trick to combine elements of two good individuals in the hopes of generating a better one and, in particular, by combining the elements that make these solutions good in isolation. The trick of recombination can be seen not only in genetic systems, but also in immune and chemical systems as well. This paper describes and explains these latter recombination mechanisms, first from a biological or chemical perspective, then from an engineering perspective. With regard to crossover in immune systems, several algorithmic mechanisms have already been proposed (e.g. IRM, GA-Simplex, STEP) and these are reviewed. Their basic functionality in each case is the same: new individuals are created in a zone of the search space that is shaped by the position of the current solutions, together with their fitness values. When the immune system proposes a new cell, the profile of this new candidate evidences a huge diversity, providing its adaptive capability, but this is subject to a subsequent "recruitment test" under the selective pressure of the current population of cells. With regard to crossover in chemical reactions, these can be viewed as a combination of computational graphs coupled with the distribution of the fitness values assigned to components in the graphs, as is already evidenced in particular instances of genetic algorithms and genetic programming. The benefits that these new features allow are discussed, along with other possible positive influences that come from chemistry. Finally, the paper shows how chemistry and immunology converge to this same basic message, which is in line with classical optimization techniques: exploit the information contained in the current population of solutions better before proposing a new candidate to be evaluated

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:6 ,  Issue: 3 )