Runtime Analysis of Evolutionary Algorithms on Randomly Constructed High-Density Satisfiable 3-CNF Formulas

2014 ◽  
pp. 942-951 ◽  
Author(s):  
Andrew M. Sutton ◽  
Frank Neumann
Keyword(s):  
Evolutionary Algorithms ◽  
High Density ◽  
Runtime Analysis ◽  
Cnf Formulas

On the Effect of Populations in Evolutionary Multi-Objective Optimisation

2010 ◽  
Vol 18 (3) ◽  
pp. 335-356 ◽  
Author(s):  
Oliver Giel ◽  
Per Kristian Lehre
Keyword(s):  
Problem Solving ◽  
Evolutionary Algorithms ◽  
Open Problem ◽  
Population Based ◽  
Runtime Analysis ◽  
Single Individual ◽  
Multi Objective ◽  
Important Open Problem ◽  
Population Based Algorithm

Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail.

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