Adaptive scheduling on unrelated machines with genetic programming

This paper investigates the use of genetic programming in automatized synthesis of heuristics for the parallel unrelated machines environment with arbitrary performance criteria. The proposed scheduling heuristic consists of a manually defined meta-algorithm which uses a priority function evolved separately with genetic programming. In this paper, several different genetic programming methods for evolving priority functions, like dimensionally aware genetic programming, genetic programming with iterative dispatching rules and gene expression programming, have been tried out and described. The performance of the suggested approach is compared to existing scheduling heuristics and it is shown that it mostly outperforms them. The described approach could prove useful when used for optimizing scheduling criteria for which no adequate scheduling heuristic exists. (C) 2016 Elsevier B.V. All rights reserved.