Chap 7: Penalty functions (1/2)

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Presentation transcript:

Chap 7: Penalty functions (1/2) Restricting the search to only feasible solutions or imposing very severe penalties makes it difficult to find the schemata that will drive the population toward the optimum. © 2011 SNU CSE Biointelligence Lab

Chap 7: Penalty functions (2/2) NFT: a near-feasible threshold  the threshold distance from the feasible region. © 2011 SNU CSE Biointelligence Lab

© 2011 SNU CSE Biointelligence Lab Chap 8: Decoders © 2011 SNU CSE Biointelligence Lab

Chap 9: Repair algorithms Knapsack problem © 2011 SNU CSE Biointelligence Lab

Chap 10: Constraint-preserving operators Specialized operators which preserve feasibility of individuals  Incorporating problem-specific knowledge. Disadvantages: (1) The problem specific operators must be tailored for a particular application; (2) it is very difficult to provide any formal analysis. The construction of the offspring starts with a selection of an initial city  The city with the smallest number of edges selected from the parents. © 2011 SNU CSE Biointelligence Lab

© 2011 SNU CSE Biointelligence Lab Chap 11: Other constraint-handling methods - Multi objective optimization methods - Goal - Constraint violation measure - Each individual x is assigned a rank r(x) and violation measure VEGA system A division of the population into subpopulations  each subpopulation was responsible for a single objective. Pareto ranking: an individual’s rank corresponds to the number of individuals in the current population. © 2011 SNU CSE Biointelligence Lab

© 2011 SNU CSE Biointelligence Lab Chap 11: Other constraint-handling methods - Coevolutionary model approach Idea of handling constraints in a particular order. © 2011 SNU CSE Biointelligence Lab

© 2011 SNU CSE Biointelligence Lab Chap 11: Other constraint-handling methods - Segregated genetic algorithm A double penalty strategy  Double population may help locate the optimal region faster. © 2011 SNU CSE Biointelligence Lab

Chap 12: Constraint-satisfaction problems - FOP, CSP, COP © 2011 SNU CSE Biointelligence Lab