Multi-objective binary search optimisation

Authors:
Evan J. Hughes
Affiliations:
Department of Aerospace, Power, and Sensors, Cranfield University, Royal Military College of Science, Shrivenham, Swindon, SN, UK
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

Citing 2
Cited 4

Voronoi diagrams—a survey of a fundamental geometric data structure

ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms

Seeking the Pareto front for multiobjective spatial optimization problems

International Journal of Geographical Information Science
Robust and efficient multi-objective automatic adjustment for optical axes in laser systems using stochastic binary search algorithm

ICES'07 Proceedings of the 7th international conference on Evolvable systems: from biology to hardware
Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Applied Soft Computing
Multiobjective optimization on a budget of 250 evaluations

EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

In complex engineering problems, often the objective functions can be very slow to evaluate. This paper introduces a new algorithm that aims to provide controllable exploration and exploitation of the decision space with a very limited number of function evaluations. The paper compares the performance of the algorithm to a typical evolutionary approach.