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 Performance of Different Genetic Programming Approaches for the SORTING Problem.

Evolutionary computation - 1 Jan 2015

Wagner Markus, Neumann Frank, Urli Tommaso

Abstract excerpt

In genetic programming, the size of a solution is typically not specified in advance, and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimization process. Consequently, problems that are relatively easy to...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Biological Evolution
Share to Computational Biology

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

Share to Computer Simulation

Share to Models, Genetic

Share to Models, Theoretical

Share to Mutation

Source

Identifiers

PMID: 25870929
DOI: 10.1162/EVCO_a_00149

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 Performance of Different Genetic Programming Approaches for the SORTING Problem.PMID 25870929Read ↗

Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives.Semantic proximity Read ↗

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.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 ↗

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

Combining convergence and diversity in evolutionary multiobjective optimization.Semantic proximity Read ↗

Fitness landscapes, memetic algorithms, and greedy operators for graph bipartitioning.Semantic proximity Read ↗

Convergence of simple genetic algorithms for the two-bit problem.Semantic proximity Read ↗

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

On the choice of the parent population size.Semantic proximity Read ↗

A rigorous complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs.Semantic proximity Read ↗

Sorting permutations by fragmentation-weighted operations.Semantic proximity Read ↗

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