Session Organizers
- Dr. Tad Gonsalves Sophia University, Tokyo, Japan This email address is being protected from spambots. You need JavaScript enabled to view it.
Description
Evolutionary Computation (EC) deals with a class of algorithms that mimic natural phenomena and/or social animals with their prey-hunting or predator-avoiding instincts. Starting with a random population of individuals, the algorithms use a fitness function to select individuals based on their performance to give rise to fitter offspring in succeeding generations. Being domain-independent, robust and suited to ill-defined problems of any arbitrary size, they are rapidly widening their areas of application.
This session welcomes the application of EC techniques to real-world applications where optimization needs to be performed in a complex and uncertain environment.
Topics for this session include but are not limited to:
- Evolutionary, Nature-inspired and Swarm Intelligence Algorithms
- Genetic Algorithm
- Differential Evolution
- Particle Swarm Optimization
- Ant Colony Optimization
- Harmony Search
- Firefly Algorithm
- Fireworks Algorithm
- Gravitational Search Algorithm
- Hybrid Evolutionary Algorithms
- Applications of EC
- Single Objective Optimization Problems (SOPs)
- Multi-Objective Optimization Problems (MOPs)
- Machine learning and data mining
- Game playing
- Optimization scenarios in engineering, business, education, bio-informatics, etc.