A Species Conserving Genetic Algorithm for Multimodal Function Optimization

By Jian-Ping Li. Evolutionary Computation, 2002

442 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/eb4a2b95162c567788c694e0aec4527ab9ee3e51

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Seyedali Mirjalili
4. Xin-She Yang
5. Seyed Mohammad Mirjalili
6. Carlos Artemio Coello Coello
7. Francisco Herrera
8. Pinar Çivicioglu

In Computational Theory and Mathematics

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Xin-She Yang
4. Seyedali Mirjalili
5. Seyed Mohammad Mirjalili
6. Carlos Artemio Coello Coello
7. Francisco Herrera
8. Pinar Çivicioglu

Citations in the corpus

• A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems (2018, background): “Numerous variations of niching methods have been proposed, including fitness sharing [7], [8], crowding [9], clearing [10], and speciation [11].”
• A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization (2013, methodology): “The procedure for determining species and the species seeds was adopted from [35].”
• Bio-inspired computation: Where we stand and what's next (2019, background): “• Speciation [163] and islanding [164] are other ways of dealing with multimodal optimization problems.”
• Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, methodology): “In total, 18 different multimodal algorithms are considered in our experiments: 1) NCDE: the neighborhood based crowding DE; 2) NSDE: the neighborhood based speciation DE; 3) NShDE: the neighborhood based sharing DE; 4) CDE [6]: the original crowding DE; 5) SDE [13]: speciation-based DE; 6) ShDE: mo”
• Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization (2004, background): “[9] on F1 (equal maxima function) and F5 (Himmelblau’s function).”
• Efficient sequential decision‐making algorithms for container inspection operations (2011, result): “Also, we observed that as opposed to the stochastic search technique, where the algorithm returned a single best tree in most of the cases, the Genetic Algorithm based search algorithm returned a whole population of trees, most of which belonged to the cheapest 50 trees.”