Simulated Learning and Genetic Programming with Application to Undecidable ProblemsUniversity of Wisconsin--Madison, 2001 - 185 pages |
Contents
Optimization | 4 |
THE CONCEPT AND METHOD | 12 |
GENETIC PROGRAMMING FOR | 40 |
Copyright | |
9 other sections not shown
Common terms and phrases
Alan Turing algorithms and heuristics applied beam search best solution branch & bound branch and bound candidate solution chromosomes cognitive combinatorial optimization combinatorial problems const double const int contains cost crossover derived sequence designing new algorithms elements evolutionary approach function Evaluate function Kernel function Select genetic algorithms genetic programming heuristic high performance improvement initial population input iteration learning for optimization learning models link information link matrix logic loop memory matrix methodology mutation necessary for B&B neighborhood search neural networks NP-complete NP-hard number of solutions Optimal Schedule Suggested Output parameter values partial schedule partial solutions Performance of Schedule position matrix problem instance random randomly representation scheme returned i.e. SBLD sel_method selection method sequencing examples sequencing problems set of solutions simulated annealing simulated learning solution sequence solution set solution space Step stimuli function Table tabu search traveling salesperson's problem Turing undecidable problems Xbad Xgood Xmeasure Xperm Xpop Xstop_condition