Enhanced Direct and Indirect Genetic Algorithm Approaches for a Mall Layout and Tenant Selection Problem

TL;DR

This paper introduces an automated method for tuning decoder weights in genetic algorithms, improving solutions for complex mall layout and tenant selection problems.

Contribution

It presents an automated approach where the genetic algorithm dynamically sets decoder weights, enhancing solution quality over traditional manual tuning methods.

Findings

Improved solution quality for complex scheduling problems

Automated weight setting outperforms manual tuning

Effective for non-linear, multi-component problems

Abstract

During our earlier research, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder, the decoder has to strike a balance between being an optimiser in its own right and finding feasible solutions. Previously this balance was achieved manually. Here we extend this by presenting an automated approach where the genetic algorithm itself, simultaneously to solving the problem, sets weights to balance the components out. Subsequently we were able to solve a complex and non-linear scheduling problem better than with a standard direct genetic algorithm implementation.