chapter 1 fundamentals and literature
1.1 optimization problems
1.2 canonical genetic algorithm
1.3 individual representations
1.4 mutation
1.5 recombination
1.6 population models
1.7 parent selection
1.8 survivor selection
1.9 summary
chapter 2 the probabilistic model -building genetic algorithms
2.1 introduction
2.2 a simple optimization example
2.3 different eda approaches
2.4 optimization in continuous domains with edas
2.5 summary
chapter 3 an empirical comparison of edas in binary search spaces
3.1 introduction
3.2 experiments
3.3 test functions for the convergence reliability
3.4 experimental results
3.5 summary
chapter 4 development of a new type of edas based on principle of maximum entropy
4.1 introduction
4.2 entropy and schemata
4.3 the idea of the proposed algorithms
4.4 how can the estimated distribution be computed and sampled?
4.5 new algorithms
4.6 empirical results
4.7 summary
chapter 5 applying continuous edas to optimization problems
5.1 introduction
5.2 description of the optimization problems
5.3 edas to test
5.4 experimental description
5.5 summary
chapter 6 optimizing curriculum scheduling problem using eda
6.1 introduction
6.2 optimization problem of curriculum scheduling
6.3 methodology
6.4 experimental results
6.5 summary
chapter 7 recognizing human brain images using edas
7.1 introduction
7.2 graph matching problem
7.3 representing a matching as a permutation
7.4 apply edas to obtain a permutation that symbolizes the solution
7.5 obtaining a permutation with continuous edas
7.6 experimental results
7.7 summary
chapter 8 optimizing dynamic pricing problem with edas and ga
8.1 introduction
8.2 dynamic pricing for resource management
8.3 modeling dynamic pricing
8.4 an ea approaches to dynamic pricing
8.5 experiments and results
8.6 summary
chapter 9 improvement techniques of edas
9.1 introduction
9.2 tradeoffs are exploited by efficiency-improvement techniques
9.3 evaluation relaxation: designing adaptive endogenous surrogates
9.4 time continuation: mutation in edas
9.5 summary