PROJECT TITLE :

Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP

ABSTRACT:

In recent decades, a plethora of dedicated evolutionary algorithms (EAs) are crafted to resolve domain-specific advanced problems additional efficiently. Several advanced EAs have relied on the incorporation of domain-specific data as inductive biases that's deemed to suit the matter of interest well. As such, the embedment of domain knowledge regarding the underlying problem at intervals the search algorithms is becoming an established mode of enhancing evolutionary search performance. During this paper, we have a tendency to gift a study on evolutionary memetic computing paradigm that's capable of learning and evolving data meme that traverses totally different but related problem domains, for greater search efficiency. Focusing on combinatorial optimization as the area of study, a realization of the proposed approach is investigated on 2 NP-laborious problem domains (i.e., capacitated vehicle routing drawback and capacitated arc routing problem). Empirical studies on well-established routing issues and their respective state-of-the-art optimization solvers are presented to review the potential advantages of leveraging information memes that are learned from completely different but related downside domains on future evolutionary search.