Open AccessControlling crossover through inductive learning
Author(s) -
Michèle Sébag,
Marc Schoenauer
Publication year - 1994
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-58484-6
DOI - 10.1007/3-540-58484-6_265
Subject(s) - crossover , population , block (permutation group theory) , computer science , control (management) , quality (philosophy) , artificial intelligence , mathematics , physics , medicine , combinatorics , environmental health , quantum mechanics
. Crossover may achieve the fast combination of performantbuilding blocks ; but as a counterpart, crossover may as well break anewly discovered building block. We propose to use inductive learningto control such disruptive effects of crossover. The idea is to periodicallygather some examples of crossovers, labelled as "good" or"bad" crossovers according to their effects on the current population.From these examples, inductive learning builds rules characterizing thecrossover quality....
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