Fitness landscapes and memetic algorithm design
New ideas in optimization
ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
New Genetic Local Search Operators for the Traveling Salesman Problem
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data
Genetic Programming and Evolvable Machines
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Finding the optimal gene order in displaying microarray data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Applying memetic algorithms to the analysis of microarray data
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Biological cluster validity indices based on the gene ontology
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Multi-Objective Genetic Algorithm for Robust Clustering with Unknown Number of Clusters
International Journal of Applied Evolutionary Computation
International Journal of Hybrid Intelligent Systems
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Microarrays have become a key technology in experimental molecular biology. They allow a monitoring of gene expression for more than ten thousand genes in parallel producing huge amounts of data. In the exploration of transcriptional regulatory networks, an important task is to cluster gene expression data for identifying groups of genes with similar patterns.In this paper, memetic algorithms (MAs) - genetic algorithms incorporating local search - are proposed for minimum sum-of-squares clustering. Two new mutation and recombination operators are studied within the memetic framework for clustering gene expression data. The memetic algorithms using a sophisticated recombination operator are shown to converge very quickly to (near-)optimum solutions. Furthermore, the MAs are shown to be superior to multi-start k-means clustering algorithms in both computation time and solution quality.