A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Randomized algorithms
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
On the Optimization of Unimodal Functions with the (1 + 1) Evolutionary Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Analysis Of The Role Of Offspring Population Size In EAs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
IEEE Transactions on Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
The one-dimensional Ising model: mutation versus recombination
Theoretical Computer Science
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
Parameter Control Methods for Selection Operators in Genetic Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Population size reduction for the differential evolution algorithm
Applied Intelligence
On the choice of the parent population size*
Evolutionary Computation
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
Theoretical analysis of fitness-proportional selection: landscapes and efficiency
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Insight knowledge in search based software testing
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Futility-based offspring sizing
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Optimization of temporal processes: a model predictive control approach
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A prime step in the time series forecasting with hybrid methods: the function choice
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Analysis of an asymmetric mutation operator
Evolutionary Computation
On the brittleness of evolutionary algorithms
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Multiobjective optimization of temporal processes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A few ants are enough: ACO with iteration-best update
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Quasirandom evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Viewing the problem from different angles: a new diversity measure based on angular distances
Journal of Artificial Evolution and Applications
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Runtime analysis of the 1-ANT ant colony optimizer
Theoretical Computer Science
Adaptive population models for offspring populations and parallel evolutionary algorithms
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
How comma selection helps with the escape from local optima
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
A large population size can be unhelpful in evolutionary algorithms
Theoretical Computer Science
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The choice of the offspring population size in the (1,λ) EA
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Adaptive population tuning scheme for differential evolution
Information Sciences: an International Journal
Computing longest common subsequences with the B-cell algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Lessons from the black-box: fast crossover-based genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
How the (1+λ) evolutionary algorithm optimizes linear functions
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.01 |
Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.