By Cyril Fonlupt, Jin-Kao Hao, Evelyne Lutton, Edmund Ronald, Marc Schoenauer
The complaints of the 4th ecu convention on synthetic Evolution, AE '99, held in Dunkerque, France, November 3-5, 1999. the various significant subject matters mentioned on the convention contain genetic operators and theoretical versions, purposes, brokers and cooperation, and heuristics. Softcover.
Read or Download Artificial Evolution: 4th European Conference, AE'99 Dunkerque, France, November 3-5, 1999 Selected Papers PDF
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Extra info for Artificial Evolution: 4th European Conference, AE'99 Dunkerque, France, November 3-5, 1999 Selected Papers
Since these measurements can be performed a priori to any EA run, inadequate encoding techniques or operators providing weak locality can be early recognized. 5, namely the first problem of Chu’s benchmarks with these parameters . Although different absolute values have been obtained as results for other instances, this specific problem is representative in the sense that the same basic tendencies have been observed for other instances, too. MI has been empirically estimated by randomly drawing 20 000 genotypes X ∈ G and applying mutation to each with the same probability or rate as in the EA.
G. described by the buildingblock hypothesis . To consider this aspect, too, we define the crossover loss CLk as the number of phenotypic properties of xc which are not inherited from either xp1 or xp2 but are newly introduced. For the MKP this means δ(xcj , xpj 1 , xpj 2 ) CLk := (7) j∈J with δ(xcj , xpj 1 , xpj 2 ) = 0 1 if xcj = xpj 1 or xcj = xpj 2 otherwise. (8) Characterizing Locality in Decoder-Based EAs 47 Table 2. 72 Considering also the proposed phenotypic distance metric, the crossover loss can alternatively be written as CLk := 1 (dP (xc , xp1 ) + dP (xc , xp2 ) − dP (xp1 , xp2 )) .
In general, a genotypic distance dG (X, Y ) = k means that at least k mutations are necessary to transform X into Y . For the considered EAs, the proposed definitions of dG (X, Y ) and dP (x, y) satisfy the metric conditions, namely identity, symmetry, and the triangular inequality. 2 Mutation Innovation MI Usually, the mutation operator is not applied exactly once but either with a given probability or a certain number of times. Let X m ∈ G represent the solution obtained from X ∈ G by applying mutation with exactly this probability or rate.