http://hdl.handle.net/10900/53296 2026-05-12T14:22:10Z http://hdl.handle.net/10900/43969 The Local Searcher as a Supplier of Building Blocks in Self-generating Memetic Algorithms Krasnogor, Natalio; Gustafson, Steven In this paper we implement a Self-Generating Memetic Algorithm for the Maximum Contact Overlap Problem (MAX-CMO). We demonstrate how the optimization of solutions can be done simultaneously with the discovering of useful local search strategies. In turn, the evolved local searchers act as suppliers of building blocks for the evolutionary algorithm. 2003-01-01T00:00:00Z http://hdl.handle.net/10900/43971 Comparison of Multiobjective Memetic Algorithms on 0/1 Knapsack Problems Ishibuchi, Hisao; Kaige, Shiori The paper compares two well-known multiobjective memetic algorithms through computational experiments on 0/1 knapsack problems. The two algorithms are MOGLS (multiple objective genetic local search) of Jaszkiewicz and M-PAES (memetic Pareto archived evolution strategy) of Knowles & Corne. It is shown that the MOGLS with a sophisticated repair algorithm based on the current weight vector in the scalar fitness function has much higher search ability than the M-PAES with a simple repair algorithm. When they use the same simple repair algorithm, the M-PAES performs better overall. It is also shown that the diversity of non-dominated solutions obtained by the MPAES is small in comparison with the MOGLS. For improving the performance of the M-PAES, we examine the use of the scalar fitness function with a random weight vector in the selection procedure of parent solutions. 2003-01-01T00:00:00Z http://hdl.handle.net/10900/43970 An Introduction to the COLIN Optimization Interface Hart, William E. We describe COLIN, a Common Optimization Library INterface for C++. COLIN provides C++ template classes that define a generic interface for both optimization problems and optimization solvers. COLIN is specifically designed to facilitate the development of hybrid optimizers, for which one optimizer calls another to solve an optimization subproblem. We illustrate the capabilities of COLIN with an example of a memetic genetic programming solver. 2003-01-01T00:00:00Z http://hdl.handle.net/10900/43972 The Compact Memetic Algorithm Merz, Peter Optimization by probabilistic modeling is a growing research field in evolutionary computation. An example is the compact genetic algorithm (cGA), in which the population of a genetic algorithm (GA) is represented as a probability distribution over the set of solutions. Both cGA algorithm and the order-one behavior of a simple GA with uniform crossover are operationally equivalent. The cGA is much easier to implement and requires less memory. In this paper, memetic algorithms (MAs) are investigated in which the population is replaced by a probability vector analogously to the cGA. The resulting compact memetic algorithms (cMAs) hence require less memory, are easier to implement and require fewer parameters than other MAs. It is shown that cMAs with and without additional recombination perform comparable to or better than population-based MAs on a set of benchmark instances of the unconstrained binary quadratic programming problem. 2003-01-01T00:00:00Z