Home > Research > Publications & Outputs > Two frameworks for cross-domain heuristic and p...

Associated organisational unit

Links

Text available via DOI:

View graph of relations

Two frameworks for cross-domain heuristic and parameter selection using harmony search

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Published
NullPointerException

Abstract

Harmony Search is a metaheuristic technique for optimizing problems involving sets of continuous or discrete variables, inspired by musicians searching for harmony between instruments in a performance. Here we investigate two frameworks, using Harmony Search to select a mixture of continuous and discrete variables forming the components of a Memetic Algorithm for cross-domain heuristic search. The first is a single-point based framework which maintains a single solution, updating the harmony memory based on performance from a fixed starting position. The second is a population-based method which co-evolves a set of solutions to a problem alongside a set of harmony vectors. This work examines the behaviour of each framework over thirty problem instances taken from six different, real-world problem domains. The results suggest that population co-evolution performs better in a time-constrained scenario, however both approaches are ultimately constrained by the underlying metaphors.