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    STUDIA INFORMATICA - Issue no. 1 / 2000  

Authors:  D. DUMITRESCU.
  Abstract:  A new evolutionary search and optimization metaheuristics, called Genetic Chromodynamics (GC), is proposed. The GC-based methods use a variable sized solution population and a local interaction principie. Local in- teractions induce a restricted mating scheme and permit detection of multiple optimal solutions. The main idea of the GC strategy is to force the formation and main- tenance of stable sub-populations. Proposed local interaction scheme ensures sub-population stabilization in the early search stages. Sub-populations co-evolve and eventually converge towards several opti- mal solutions. The number of individuals in the current population decreases with the generation. Very close individuals are merged. At convergence the number of sub-populations equals the number of optimal solutions. Each final sub-population hopefully contains a single individual representing an optimum point (a solution of the problem). The GC approach allows as solution representation any data structure compatible with the problem and any set of meaningful variation operators. GC-based techniques can be used to solve multimodal, static and dy- namic, optimization problems.  
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