Opposition-Based Learning: A New Scheme for Machine Intelligence

By Hamid R. Tizhoosh. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005

2,080 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/01f702f8b1f9d1314587015f1f038af4d5735e77

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. Shahryar Rahnamayan
2. Sedigheh Mahdavi
3. Mohammad Ebrahim Shiri
4. Kalyanmoy Deb
5. Magdy M. A. Salama
6. Dawei Du
7. D. Simon
8. Mehmet Ergezer

In Control and Systems Engineering

1. Shahryar Rahnamayan
2. Magdy M. A. Salama
3. Fariborz Jolai
4. Maziar Yazdani
5. Sedigheh Mahdavi
6. Hao Gao
7. Mohammad Ebrahim Shiri
8. Hui Wang

Citations in the corpus

• Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model (2021, methodology): “Besides the basic PSO algorithm modification, other variations in PSO performance improvement were also made in (Tizhoosh 2005).”
• A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, background): “By building upon the concepts of a free search [40], DE, and opposition-based learning [41], free-search DE”
• Opposition versus randomness in soft computing techniques (2008, background): “Hence, the point and its opposite point are evaluated simultaneously to continue with the fitter one [3].”
• An improved differential evolution algorithm and its application in optimization problem (2021, background): “Opposition-based learning (OBL) is a new intelligence strategy proposed by Tizhoosh (2005).”
• Quasi-oppositional Differential Evolution (2007, background): “Before concentrating on quasi-oppositional version of DE, we need to define the concept of opposite numbers [18]: ] be a real number.”
• Oppositional biogeography-based optimization (2009, background): “Opposition-based learning (OBL) is proposed in [7].”
• Opposition based learning: A literature review (2017, background): “Therefore, the computational opposition concept [297] was inspired from the opposition concept in the real-world and the opposite numbers were simply defined in [297] as follows.”
• Application of Opposition-Based Reinforcement Learning in Image Segmentation (2007, background): “PSEUDO CODE OF OB Q-LEARNING FOR OPPOSITE ACTION [ 7 ].”
• Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm (2016, positive): “The basic concept of Opposition-Based Learning (OBL) was proposed by Tizhoosh [61] and has been proven to be an effective method for solving optimization problems.”
• A novel population initialization method for accelerating evolutionary algorithms (2007, methodology): “The concept of opposition-based learning was introduced by Tizhoosh [9] and its applications were introduced in [9–11].”