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A significantly under-explored area of evolutionary optimization in theliterature is the study of optimization methodologies that can evolve alongwith the problems solved. Particularly, present evolutionary optimizationapproaches generally start their search from scratch or the ground-zero stateof knowledge, independent of how similar the given new problem of interest isto those optimized previously. There has thus been the apparent lack ofautomated knowledge transfers and reuse across problems. Taking the cue, thispaper introduces a novel Memetic Computational Paradigm for search, one thatmodels after how human solves problems, and embarks on a study towardsintelligent evolutionary optimization of problems through the transfers ofstructured knowledge in the form of memes learned from previous problem-solvingexperiences, to enhance future evolutionary searches. In particular, theproposed memetic search paradigm is composed of four culture-inspiredoperators, namely, Meme Learning, Meme Selection, Meme Variation and MemeImitation. The learning operator mines for memes in the form of latentstructures derived from past experiences of problem-solving. The selectionoperator identifies the fit memes that replicate and transmit across problems,while the variation operator introduces innovations into the memes. Theimitation operator, on the other hand, defines how fit memes assimilate intothe search process of newly encountered problems, thus gearing towardsefficient and effective evolutionary optimization. Finally, comprehensivestudies on two widely studied challenging well established NP-hard routingproblem domains, particularly, the capacitated vehicle routing (CVR) andcapacitated arc routing (CAR), confirm the high efficacy of the proposedmemetic computational search paradigm for intelligent evolutionary optimizationof problems.

Meme as Building Block for Evolutionary Optimization of Problem Instances