A new and universal penalty method is introduced in this contribution. It is especially intended to be applied in stochastic metaheuristics like genetic algorithms, particle swarm...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
This paper presents three techniques for using an iterated local search algorithm to improve the performance of a state-of-the-art branch and bound algorithm for job shop scheduli...
We address the problem of finding the most likely assignment or MAP estimation in a Markov random field. We analyze the linear programming formulation of MAP through the lens of...
: This paper describes the implementation of a meta-heuristic optimization approach, Tabu Search (TS), for Heat Exchanger Networks (HEN) synthesis and compares this approach to oth...