The design of population-based search algorithms of evolutionary computation (EC) has traditionally been focused on efficiently solving a single optimization task at a time. It is only very recently that a new paradigm in EC, namely, multifactorial optimization (MFO), has been introduced to explore the potential of evolutionary multitasking (Gupta A et al., IEEE Trans Evol Comput ). The nomenclature signifies a multitasking search involving multiple optimization tasks at once, with each task contributing a unique factor influencing the evolution of a single population of individuals. MFO is found to leverage the scope for implicit genetic transfer offered by the population in a simple and elegant manner, thereby opening doors to a plethora of new research opportunities in EC, dealing, in particular, with the exploitation of underlying synergies between seemingly unrelated tasks. A strong practical motivation for the paradigm is derived from the rapidly expanding popularity of cloud computing (CC) services. It is noted that CC characteristically provides an environment in which multiple jobs can be received from multiple users at the same time. Thus, assuming each job to correspond to some kind of optimization task, as may be the case in a cloud-based on-demand optimization service, the CC environment is expected to lend itself nicely to the unique features of MFO. In this talk, the formalization of the concept of MFO is first introduced. A fitness landscape-based approach towards understanding what is truly meant by there being underlying synergies (or what we term as genetic complementarities) between optimization tasks is then discussed. Accordingly, a synergy metric capable of quantifying the complementarily, which shall later be shown to act as a “qualitative” predictor of the success of multitasking is also presented (Gupta A et al., A study of genetic complementarity in evolutionary multitasking ). With the above in mind, a novel evolutionary algorithm (EA) for MFO is proposed, one that is inspired by bio-cultural models of multi-factorial inheritance, so as to best harness the genetic complementarity between tasks. The salient feature of the algorithm is that it incorporates a unified solution representation scheme which, to a large extent, unites the fields of continuous and discrete optimization. The efficacy of the proposed algorithm and the concept of MFO in general, shall finally be substantiated via a variety of computation experiments in intra and inter-domain evolutionary multitasking.
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- Towards Evolutionary Multitasking: A New Paradigm in Evolutionary Computation
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