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A study on meme propagation in multimemetic algorithms

100%

Nogueras R. , Cotta C.

International Journal of Applied Mathematics and Computer Science

2015

tom 25

nr 3

499-512

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grid topologies. We show that the increased takeover time induced by the latter is essential for improving the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates. Experiments realized with an actual MMA on three different complex pseudo-Boolean functions are consistent with these findings, indicating that memes are more successful in a spatially structured MMA, rather than in a panmictic MMA, and that the performance of the former is significantly better than that of its panmictic counterpart.

Interactive multiobjective optimization with the Pareto memetic algorithm

100%

Jaszkiewicz A.

2007

tom 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

100%

Cotta C.

2006

tom 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.

An agent-oriented hierarchic strategy for solving inverse problems

88%

Smołka M. , Schaefer R. , Paszyński M. , Pardo D. , Álvarez-Aramberri J.

International Journal of Applied Mathematics and Computer Science

2015

tom 25

nr 3

483-498

The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step. The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems' difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified.