Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

On the choice of the parent population size.

Evolutionary computation - 1 Jan 2008

Storch Tobias

Abstract excerpt

Evolutionary algorithms (EAs) are population-based randomized search heuristics that often solve problems successfully. Here the focus is on the possible effects of changing the parent population size in a simple, but still realistic, mutation-based EA. It preserves diversity by avoiding duplicates in its population. On the one hand its behavior on well-known pseudo-Boolean example functions is investigated by...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artificial Intelligence
Share to Biological Evolution
Share to Algorithms

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Information Storage and Retrieval

Share to Models, Statistical

Share to Pattern Recognition, Automated

Share to Population Density

Share to Probability

Source

Identifiers

PMID: 19053499
DOI: 10.1162/evco.2008.16.4.557

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

On the choice of the parent population size.PMID 19053499Read ↗

On the choice of the offspring population size in evolutionary algorithms.Semantic proximity Read ↗

Runtime analysis of the (mu+1) EA on simple Pseudo-Boolean functions.Semantic proximity Read ↗

An Analytical Framework for Runtime of a Class of Continuous Evolutionary Algorithms.Semantic proximity Read ↗

On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism.Semantic proximity Read ↗

On Proportions of Fit Individuals in Population of Mutation-Based Evolutionary Algorithm with Tournament Selection.Semantic proximity Read ↗

A FORTRAN program to simulate the evolution of genetic variability in a small population.Semantic proximity Read ↗

Through the interaction of neutral and adaptive mutations, evolutionary search finds a way.Semantic proximity Read ↗

The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization.Semantic proximity Read ↗

Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: a case study for the multidimensional knapsack problem.Semantic proximity Read ↗

A normed space of genetic operators with applications to scalability issues.Semantic proximity Read ↗

Borg: an auto-adaptive many-objective evolutionary computing framework.Semantic proximity Read ↗

Socio-inspired Evolutionary Algorithms: A Unified Framework and SurveySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.