Adaptive global optimization with local search
Adaptive global optimization with local search
Computers and Operations Research
Memetic algorithms: a short introduction
New ideas in optimization
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Theoretical Computer Science - Natural computing
Locally-adaptive and memetic evolutionary pattern search algorithms
Evolutionary Computation
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Ant Colony Optimization
A Study on the use of "self-generation'' in memetic algorithms
Natural Computing: an international journal
Evolutionary Computation - Special issue on magnetic algorithms
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
On the analysis of the (1+1) memetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Analysis of a multiobjective evolutionary algorithm on the 0-1 knapsack problem
Theoretical Computer Science
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Worst case and probabilistic analysis of the 2-Opt algorithm for the TSP: extended abstract
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
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
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Memetic algorithms with partial lamarckism for the shortest common supersequence problem
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Efficient and experimental meta-heuristics for MAX-SAT problems
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Local search in evolutionary algorithms: the impact of the local search frequency
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineering Applications of Artificial Intelligence
An intelligent multi-restart memetic algorithm for box constrained global optimisation
Evolutionary Computation
A differential memetic algorithm
Artificial Intelligence Review
Hi-index | 5.23 |
Memetic (evolutionary) algorithms integrate local search into the search process of evolutionary algorithms. As computational resources have to be spread adequately among local and evolutionary search, one has to care about when to apply local search and how much computational effort to devote to local search. Often local search is called with a fixed frequency and run for a fixed number of iterations, the local search depth. There is empirical evidence that these parameters have a significant impact on performance, but a theoretical understanding as well as concrete design guidelines are missing. We initiate the rigorous theoretical analysis of memetic algorithms. To this end, we consider a simple memetic algorithm for pseudo-Boolean optimization that captures basic working principles of memetic algorithms-the interplay of genetic operators like mutation and selection with local search. We present function classes where even small changes of the parametrization have a strong impact on performance. For almost every reasonable parameter setting we construct a function that, with high probability, can be optimized in polynomial time. However, changing the local search depth by a small additive term in any direction yields a superpolynomial optimization time, with high probability. For another class of functions altering the local search frequency by a factor of 2 even yields exponential optimization times. Our results show exemplarily that parametrizing memetic evolutionary algorithms can be extremely hard. Moreover, this work yields insights into the dynamic behavior of memetic algorithms and contributes to a theoretical foundation of hybrid metaheuristics.