OPÉRATEURS GÉNÉTIQUES SPÉCIALISÉS 4.2. Opérateurs Procédure arc-mutation (chromosome) Début Pour chaque variable A", Générer un nombre aléatoire r entre [0..1J si r <probabilité de mutation alors I(A; / ) = l i(I-.r J " !1 )Ur,))}'» Fin /* procédure arc-mutation */ '' sa valeur courante, en anglais: backtrack 2. en anglais: look-ahead 4 ,
3 -Structure de la procédure are-mutation sont D, ? {1,2.3},V?' ? 1....4. Supposons que nous voulons changer la valeur de lu variable A" s du chromosome, qui actuellement a la valeur 2. L'ensemble M 3 sera composé par {d, Nous avons donc deux valeurs possibles pour A3 soit 1 soit ,
= 1,1) = 5 et mff(A 3 = 3,1) = 4, arc-mutation choisira la valeur 3 pour A';-¡. En faisant cela, ce chromosome sera plus facile à réparer ensuite, en lui chauiz;eant, par exemple ,
A discrete stochastic neural network algorithm for constraint satisfaction problems, Proceedings International Joint Conference on Neural NetworksAS94] J.M. Alliot and T. Schiex. Intelligence Artificielle & Informatique Théorique . Cépaduès Éditions, pp.65-67, 1990. ,
Application de la méthode score(fd/i) aux esps binaires, Sème Conference Nationale pour la Resolution Pratique de Problèmes NP-Complets, pp.75-80, 1997. ,
Scheduling under resource constraints: Deterministic models, Annals of Operations Research, 1986. ,
Arc-consistency and arc-consistency again, In Artificial Intelligence, vol.65, pp.179-190, 1994. ,
Algorithms as Function Optimizers, 1980. ,
Using inference to reduce arc consistency computation, International Joint Conference on Artificial Intelligence, pp.592-598, 1995. ,
Le recuit simulé, Pour la Science, 1988. ,
The ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Mar, and Cybernetics jDor92] 3VI. Dorigo. Optimization. Learning and Natural Algorithms, 1992. ,
Network-based heuristics for constraint-satisfaction problems, Eib9G| A.E. Eiben. Handbook of Evolutionary Computation, chapter Constraint Satisfaction Problems, pp.1-38, 1988. ,
DOI : 10.1016/0004-3702(87)90002-6
Solving constraint satisfaction problems using genetic algorithms, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp.542-547 ,
DOI : 10.1109/ICEC.1994.350002
Ga-easy and ga-hard constraint satisfaction problems, Meyer [Mey95j, pp.267-283 ,
DOI : 10.1007/3-540-59479-5_30
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.9820
Self-adaptivity for constraint satisfaction: learning penalty functions, Proceedings of IEEE International Conference on Evolutionary Computation, pp.258-261 ,
DOI : 10.1109/ICEC.1996.542371
Solving 3-sat by gas adapting constraint weights, Porto [Por97], pp.81-86 ,
Genetic algorithms and hybrids for graph coloring, Annals of Operations Research, 1992. ,
Incorporating problem specif knowledge into genetic algorithms, Genetic Algorithms and Simulated Annealing, 1987. ,
A users guide to tabu search The steepest ascend mildest descend heuristic for combinatorial programming, Annals of Operations Research Congress on Numerical Methods in Combinatorial Optimisation, pp.3-28, 1986. ,
Die Tabu-Methoden zur Graphenf??rbung, Computing, pp.345-351, 1987. ,
DOI : 10.1007/BF02239976
Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence, vol.14, issue.3, pp.263-313, 1980. ,
DOI : 10.1016/0004-3702(80)90051-X
Constrained Optimization Via Genetic Algorithms, Simulation, pp.242-254, 1994. ,
DOI : 10.1177/003754979406200405
Adaptation in evolutionary computation: a survey, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), pp.65-69 ,
DOI : 10.1109/ICEC.1997.592270
Examinations on the algebra of genetic algorithms, 1993. ,
Genetic Algorithms and the Optimal Allocation of Trials, SIAM Journal on Computing, vol.2, issue.2, pp.88-105, 1973. ,
DOI : 10.1137/0202009
Adaptation in Natural and Artificial Systems, 1975. ,
Simulated annealing and tabu search for constraint solving, 5th Intl. Symposium on Artificial Intelligence and Mathematics, 1998. ,
Cyclic-clustering: a compromise between tree-clustering and the cycle-cutset method for improving search efficiency, Proceedings of EC A190, pp.369-371, 1990. ,
On the use of non-stationary penalty function to solve non-linear constraint optimization problems with gas, Michalewicz et al, pp.579-584 ,
The Traveling Salesman Problem: A Case Study in Local Optimization, |Kar95] H. Kaxgupta. {SEARCH}, polynomial complexity, and the fast messy genetic algorithm, 1995. ,
Optimization by simulated annealing, Science, pp.671-680, 1983. ,
Hierarchical genetic algorithms operating on populations of computer programs, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp.768-774, 1989. ,
Algorithms for constraint satisfaction problems:a survey The vehicle routing problem: An overview of exact and approximate algorithms, Artificial Intelligence Magazine, pp.32-44, 1992. ,
An Effective Heuristic Algorithm for the Traveling-Salesman Problem, Operations Research, vol.21, issue.2, pp.498-516, 1973. ,
DOI : 10.1287/opre.21.2.498
Representational issues in genetic optimization, In Journal of Experimental and Theomcal Artificial Intelligence, vol.2, pp.4-30, 1990. ,
Handling constraints in genetic algorithms, Proceedings of the Fourth International Conference on Genetic Algorithms, pp.151-157, 1991. ,
Arc and path consistency revisited, In Artificial Intelligence, vol.28, pp.225-233, 1986. ,
DOI : 10.1016/0004-3702(86)90083-4
URL : https://hal.archives-ouvertes.fr/inria-00548487
Genetic Algorithms -h Data Structures = Evolution Programs . Artificial Intelligence, 1994. ,
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems, In Artificial Intelligence, vol.58, pp.161-205, 1992. ,
Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints, Proceedings of 1995 IEEE International Conference on Evolutionary Computation ,
DOI : 10.1109/ICEC.1995.487460
The breakout method for escaping from local optima, Proceedings of the AAAI, pp.40-45, 1993. ,
Handbook of Evolutionary Computation , chapter Evolutionary Algorithms for Constrained Parameter Optimization Problems, 1996. ,
Epistasis and deceptivity ,
Co-evolutionary constraint satisfaction, Third International Conference on Parallel Problem Solving Nature, pp.46-55, 1994. ,
DOI : 10.1007/3-540-58484-6_249
Heuristic search theory: a survey of recent results, International Joint Conference on Artificial Intelligence Third IEEE International Conference on Evolutionary Computation, pp.24-28, 1981. ,
Binary constraint satisfaction problems: Some are harder than others Equivalence class analysis of genetic algorithms The algebra of genetic algorithms. Epce-tr-92-11, 11th European Conference on Artificial Intelligence Genetic Neural Networks on MIMD Computers Complex Systems jRawBlj G.J. R.awiins. Foundations of Genetic Algorithms, pp.46-91, 1990. ,
On the equivalence of constraint satisfaction problem. Act-ai-222-89, 1989. ,
An analysis of a simple genetic algorithm Stochastic search and phase de transitions: Ai meets physics, Proceedings of the Fourth International Conference on Genetic Algorithms International Joint Conference on Artificial Intelligence, pp.215-222, 1981. ,
Noise strategies for improving local search, Proceedings of the. AAAI, pp.337-343, 1994. ,
A new method for solving hard satifiability problems In search of exceptionally difficult constraint satisfaction problems, Tenth National Conference on Artificial ' ' " Intelligence Meyer [Mey95j, pp.440-446, 1992. ,
Genetic optimization using a penalty function, Proceedings of the Fifth International Conference on Genetic Algorithms |Svs89j G. Syswerda. Uniform crossover in genetic algorithmes Third International Conference on Genetic Algorithms, pp.499-503, 1968. ,
Robust tabu search for the quadratic assignment problem Parallel iterative search methods for vehicle routing problems Genetic algorithms versus simulated annealing: Satisfaction of large sets of algebraic mechanical design constraints Applying genetic algorithms to constraint satisfaction optimization problems, Nineth European Conference on Artificial Intelligence, pp.443-455, 1989. ,
Simulated Annealing: Theory and Applications . Klinver, 1988. |War95¡ T. Warwick. .4 GA Approach to Constraint Satisfaction Problems, 1995. ,
Using a genetic algorithm to tackle the processors configuration problem, Proceedings of the 1994 ACM symposium on Applied computing , SAC '94, pp.217-221, 1994. ,
DOI : 10.1145/326619.326726