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Interactive multiobjective optimization with the Pareto memetic algorithm

Jaszkiewicz A.

Foundations of Computing and Decision Sciences

2007

Vol. 32, No. 1

15-31

The paper describes an interactive multiobjective memetic algorithm. During the run of the method the DM is periodically asked to compare a pair of generated solutions. The comparisons are used to focus the search in the promising region of the nondorninated set. The algorithm is evaluated on the multiobjective traveling salesperson problem with four, five and six objectives. It is also compared to an interactive evolutionary metaheuristic proposed by Phelps and Koksalan. The results of the computational experiment indicate that the interactive algorithm can efficiently find high quality solutions even in the case of multidimensional objective space.

From Genes to Memes: Optimization by Problem-aware Evolutionary Algorithms

Cotta C.

Schedae Informaticae

2006

Vol. 15

127-150

Memetic algorithms are population-based metaheuristics aimed to solve hard optimization problems. These techniques are explicitly concerned with exploiting available knowledge in order to achieve the most effective resolution of the target problem. The rationale behind this optimization philosophy, namely the intrinsic theoretical limitations of problem-unaware optimization techniques, is presented in this work. A glimpse of the main features of memetic algorithms, and a brief overview of the numerous applications of these techniques is provided as well.