الصفحة 1
الصفحة 1
Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems ; Vol.3990 ; 3rd International Conference, CPAIOR 2006, Cork, Ireland, May 31 - June 2, 2006, Proceedings
Constitutes the refereed proceedings of the Third International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2006. The 20 revised full papers presented together with 3 invited talks address methodological and foundational issues from AI, OR, and algorithmics and present applications to the solution of combinatorial optimization problems in various fields via constraint programming.
Evolutionary computation in combinatorial optimization ; 8th European Conference, EvoCOP 2008, Naples, Italy, March 26-28, 2008. Proceedings
Metaheuristics have been shown to be e?ective for di?cult combinatorial - timization problems appearing in various industrial, economical, and scientifc domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman pr- lem, packing and cutting, satisfability and general mixed integer programming.
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics ; International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings
Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-specific background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, artificial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.
