1. 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.
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2. Chap 7: Penalty functions (2/2)
NFT: a near-feasible threshold  the threshold distance from the feasible region.
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Chap 8: Decoders
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4. Chap 9: Repair algorithms
Knapsack problem
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5. 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

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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.
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Chap 11: Other constraint-handling methods - Coevolutionary model approach
Idea of handling constraints in a particular order.
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8. © 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.
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9. Chap 12: Constraint-satisfaction problems - FOP, CSP, COP
© 2011 SNU CSE Biointelligence Lab