6 papers
Error Analysis of Elitist Randomized Search Heuristics
Cong Wang, Yu Chen, Jun He +1
When globally optimal solutions of complicated optimization problems cannot be located by evolutionary algorithms (EAs) in polynomial expected running time, the hitting time/runnin…
Helper and Equivalent Objectives: An Efficient Approach to Constrained Optimisation
Tao Xu, Jun He, Changjing Shang
Numerous multi-objective evolutionary algorithms have been designed for constrained optimisation over past two decades. The idea behind these algorithms is to transform constrained…
A Theoretical Framework of Approximation Error Analysis of Evolutionary Algorithms
Jun He, Yu Chen, Yuren Zhou
In the empirical study of evolutionary algorithms, the solution quality is evaluated by either the fitness value or approximation error. The latter measures the fitness difference…
Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization
Wei Huang, Tao Xu, Kangshun Li +1
Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators…
New Methods of Studying Valley Fitness Landscapes
Jun He, Tao Xu
The word "valley" is a popular term used in intuitively describing fitness landscapes. What is a valley on a fitness landscape? How to identify the direction and location of a vall…
Analysis of Solution Quality of a Multiobjective Optimization-based Evolutionary Algorithm for Knapsack Problem
Jun He, Yong Wang, Yuren Zhou
Multi-objective optimisation is regarded as one of the most promising ways for dealing with constrained optimisation problems in evolutionary optimisation. This paper presents a th…