Background: Purely epistatic multi-locus interactions cannot generally be detected via singlelocus analysis in case-control studies of complex diseases. Recently, many two-locus and multilocus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.

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This paper provides a review on current developments in genetic algorithms. The discussion includes theoretical aspects of genetic algorithms and genetic algorithm applications. Theoretical topics under review include genetic algorithm techniques, genetic operator technique, niching techniques, genetic drift, method of benchmarking genetic algorithm performances, measurement of difficulty level of a test-bed function, population genetics and developmental mechanism in genetic algorithms. Examples of genetic algorithm application in this review are pattern recognition, robotics, artificial life, expert system, electronic circuit design, cellular automata, and biological applications. While the paper covers many works on the theory aiid application of genetic algorithms, not much details are reported on genetic programming, parallel genetic algorithms, in addition to more advanced techniques e.g. micro-genetic algorithms and multiobjective optimisation.