Abstract
Many semantic search based on Genetic Programming (GP) use a trial-and-error scheme to attain semantically diverse offspring in the evolutionary search. This results in significant impediments on the success of semantic-based GP in solving real world problems, due to the additional computational overheads incurred. This paper proposes a surrogate Genetic Programming (or sGP in short) to retain the appeal of semantic-based evolutionary search for handling challenging problems with enhanced efficiency. The proposed sGP divides the population into two parts (mu and lambda) then it evolves mu percentage of the population using standard GP search operators, while the remaining 2 percentage of the population are evolved with the aid of meta-models (or approximation models) that serve as surrogate to the original objective function evaluation (which is computationally intensive). In contrast to previous works, two forms of meta-models are introduced in this study to make the idea of using surrogate in GP search feasible and successful. The first denotes a "Semanticmodel" for prototyping the semantic representation space of the GP trees (genotype/syntactic-space). The second is a "Fitness-model", which maps solutions in the semantic space to the objective or fitness space. By exploiting the two meta-models collectively in serving as a surrogate that replaces the original problem landscape of the GP search process, more cost-effective generation of offspring that guides the search in exploring regions where high quality solutions resides can then be attained. Experimental studies covering three separate GP domains, namely, (1) Symbolic regression, (2) Even n-parity bit, and (3) a real-world Time-series forecasting problem domain involving three datasets, demonstrate that sGP is capable of attaining reliable, high quality, and efficient performance under a limited computational budget. Results also showed that sGP outperformed the standard GP, GP based on random training-set technique, and GP based on conventional data-centric objectives as surrogate. (C) 2014 Elsevier Inc. All rights reserved.