95 citations · 308 across the 24 of their papers we have counts for
24 papers
Influence of Binomial Crossover on Approximation Error of Evolutionary Algorithms
Cong Wang, Jun He, Yu Chen +1
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working prin…
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…
Average Convergence Rate of Evolutionary Algorithms II: Continuous Optimization
Yu Chen, Jun He
The average convergence rate (ACR) measures how fast the approximation error of an evolutionary algorithm converges to zero per generation. It is defined as the geometric average o…
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…
An Analytic Expression of Relative Approximation Error for a Class of Evolutionary Algorithms
Jun He
An important question in evolutionary computation is how good solutions evolutionary algorithms can produce. This paper aims to provide an analytic analysis of solution quality in…
Multi-objective Differential Evolution with Helper Functions for Constrained Optimization
Tao Xu, Jun He
Solving constrained optimization problems by multi-objective evolutionary algorithms has scored tremendous achievements in the last decade. Standard multi-objective schemes usually…
Average Convergence Rate of Evolutionary Algorithms
Jun He, Guangming Lin
In evolutionary optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rate. This paper proposes…
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…
Performance Analysis on Evolutionary Algorithms for the Minimum Label Spanning Tree Problem
Xinsheng Lai, Yuren Zhou, Jun He +1
Some experimental investigations have shown that evolutionary algorithms (EAs) are efficient for the minimum label spanning tree (MLST) problem. However, we know little about that…
A Theoretical Assessment of Solution Quality in Evolutionary Algorithms for the Knapsack Problem
Jun He, Boris Mitavskiy, Yuren Zhou
Evolutionary algorithms are well suited for solving the knapsack problem. Some empirical studies claim that evolutionary algorithms can produce good solutions to the 0-1 knapsack p…
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
Jun He, Feidun He, Hongbin Dong
The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions with…
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
Boris Mitavskiy, Jun He
In the current work we introduce a novel estimation of distribution algorithm to tackle a hard combinatorial optimization problem, namely the single-machine scheduling problem, wit…
Average Drift Analysis and Population Scalability
Jun He, Xin Yao
This paper aims to study how the population size affects the computation time of evolutionary algorithms in a rigorous way. The computation time of an evolutionary algorithm can be…
Geiringer Theorems: From Population Genetics to Computational Intelligence, Memory Evolutive Systems and Hebbian Learning
Boris Mitavskiy, Elio Tuci, Chris Cannings +2
The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evo…
A Further Generalization of the Finite-Population Geiringer-like Theorem for POMDPs to Allow Recombination Over Arbitrary Set Covers
Boris Mitavskiy, Jun He
A popular current research trend deals with expanding the Monte-Carlo tree search sampling methodologies to the environments with uncertainty and incomplete information. Recently a…
Combining Drift Analysis and Generalized Schema Theory to Design Efficient Hybrid and/or Mixed Strategy EAs
Boris Mitavskiy, Jun He
Hybrid and mixed strategy EAs have become rather popular for tackling various complex and NP-hard optimization problems. While empirical evidence suggests that such algorithms are…
Mixed Strategy May Outperform Pure Strategy: An Initial Study
Jun He, Wei Hou, Hongbin Dong +1
In pure strategy meta-heuristics, only one search strategy is applied for all time. In mixed strategy meta-heuristics, each time one search strategy is chosen from a strategy pool…
On the Easiest and Hardest Fitness Functions
Jun He, Tianshi Chen, Xin Yao
The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, the study can help understanding the ability of evolutionary a…
A Polynomial Time Approximation Scheme for a Single Machine Scheduling Problem Using a Hybrid Evolutionary Algorithm
Boris Mitavskiy, Jun He
Nowadays hybrid evolutionary algorithms, i.e, heuristic search algorithms combining several mutation operators some of which are meant to implement stochastically a well known tech…
Pure Strategy or Mixed Strategy?
Jun He, Feidun He, Hongbin Dong
Mixed strategy EAs aim to integrate several mutation operators into a single algorithm. However few theoretical analysis has been made to answer the question whether and when the p…
Novel Analysis of Population Scalability in Evolutionary Algorithms
Jun He, Tianshi Chen, Boris Mitavskiy
Population-based evolutionary algorithms (EAs) have been widely applied to solve various optimization problems. The question of how the performance of a population-based EA depends…