Memetic algorithms: a short introduction
New ideas in optimization
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
Real royal road functions: where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
Memetic algorithms with variable-depth search to overcome local optima
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the choice of the parent population size*
Evolutionary Computation
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
Local search in evolutionary algorithms: the impact of the local search frequency
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Hybridizing evolutionary algorithms with opportunistic local search
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Memetic algorithms are evolutionary algorithms incorporating local search to increase exploitation. This hybridization has been fruitful in countless applications. However, theory on memetic algorithms is still in its infancy.Here, we introduce a simple memetic algorithm, the (1+1) Memetic Algorithm (1+1(MA)), working with a population size of 1 and no crossover. We compare it with the well-known (1+1) EA and randomized local search and show that these algorithms can outperform each other drastically.On problems like, e.g., long path problems it is essential to limit the duration of local search. We investigate the (1+1) MA with a fixed maximal local search duration and define a class of fitness functions where a small variation of the local search duration has a large impact on the performance of the (1+1) MA.All results are proved rigorously without assumptions.