Projects per year
Abstract
A pure strategy metaheuristic is one that applies the same search method at each generation of the algorithm. A mixed strategy metaheuristic is one that selects a search method probabilistically from a set of strategies at each generation. For example, a classical genetic algorithm, that applies mutation with probability 0.9 and crossover with probability 0.1, belong to mixed strategy heuristics. A (1+1) evolutionary algorithm using mutation but no crossover is a pure strategy metaheuristic. The purpose of this paper is to compare the performance between mixed strategy and pure strategy metaheuristics. The main results of the current paper are summarised as follows. (1) We construct two novel mixed strategy evolutionary algorithms for solving the 01 knapsack problem. Experimental results show that the mixed strategy algorithms may find better solutions than pure strategy algorithms in up to 77.8% instances through experiments. (2) We establish a sufficient and necessary condition when the expected runtime time of mixed strategy metaheuristics is smaller that that of pure strategy mixed strategy metaheuristics
Original language  English 

Title of host publication  2013 IEEE Congress on Evolutionary Computation (CEC) 
Publisher  IEEE Press 
Pages  562569 
ISBN (Electronic)  9781479904525 
ISBN (Print)  9781479904532 
DOIs  
Publication status  Published  01 Jun 2013 
Event  2013 IEEE Congress on Evolutionary Computation (CEC)  Cancun, Mexico Duration: 20 Jun 2013 → 23 Jun 2013 
Conference
Conference  2013 IEEE Congress on Evolutionary Computation (CEC) 

Country/Territory  Mexico 
City  Cancun 
Period  20 Jun 2013 → 23 Jun 2013 
Fingerprint
Dive into the research topics of 'Mixed strategy may outperform pure strategy: An initial study'. Together they form a unique fingerprint.Projects
 1 Finished

Evolutionary Approximation Algorithms for Optimization: Algorithm design and Complexity Analysis
He, J.
Engineering and Physical Sciences Research Council
01 May 2011 → 31 Oct 2015
Project: Externally funded research