top of page

Nature Inspired Algorithms

Our research is concentrated on two major areas involving the nature-inspired Algorithms

Evolutionary Algorithms

These algorithms are based on the Darwinian metaphor. Living beings continuously evolve to adjust themselves to the ever-changing environmental conditions. The fully evolved living beings possess various optimized facets which make them successful in adapting to their environment. Evolutionary Algorithms mimic the Darwinian paradigm of the survival of the fittest. They begin by randomly generating a population of feasible solutions to a given optimization problem. The population is then subjected to a handful of operators like selection, crossover, and mutation to give rise to fitter (optimized) solutions through a large number of generation cycles.

about genetic algorithm(GA)

Genetic Algorithm (GA)

The is a typical Evolutionary Algorithm  based on the Darwinian theory of natural selection and survival of the fittest. It beings with a population of randomly-generated solutions to an (optimization) problem and through a computational process mimicking the natural processes of fitness evaluation,  biological selection, crossover and mutation, "evolves" them into optimized solutions.

Artificial Immune System (AIS)

This algorithm which  imitates the human immune system is widely used in pattern matching and optimization. Antibodies  which match the  antigens undergo cloning and mutation. This improves the  defense mechanism of the antibodies.

Harmony Search Algorithm (HSA)

The Harmony Search Algorithm is based on the metaphor of how musicians fine tune (or evolve) their notes in conjunction with others to produce a harmonious melody. We have used the HS algorithm to evolve a computer program containing a Neural Network to learn to play the game of Othello.

Play against AI agent


A swarm is a large number of homogenous, unsophisticated agents that interact locally among themselves and their environment without any central control or management. The collective behavior of self-organized, but decentralized natural or artificial systems that leads to the solution of complex problems is called Swarm Intelligence. The individuals that make up the swarm are often extremely simple agents, that lack memory, intelligence or even awareness of one another. By following simple rules like sticking together and avoiding collision, they give rise to a form of emergent intelligence.

Particle Swarm Optimization (PSO)

This algorithm imitates the  feeding behavior of fish, birds, ants. The individuals in a swarm randomly look out for food sources. Through intelligent communication they find the best location  of food sources.

Ant Colony Optimization (ACO)

FireWorks Algorithm (FA)

Hybrid Swarm Intelligence

Hybrid Swarm Intelligence

bottom of page