CS440 Computer Science Seminar Introduction to Evolutionary Computing

Adaptation to environment

Traveling salesman problem A salesperson must visit clients in different cities, and then return home. What is the shortest tour through those cities, visiting each one once and only once? No known algorithms are able to generate the best answer in an amount time that grow only as a polynomial function of the number of elements (cities) in the problem. Belongs in the NP-hard class of problems, where NP stands for non-deterministic polynomial. For 100 cities, there are over different possible paths through all cities. The Universe is only seconds old!

Evolution Algorithm

Steps of evolutionary approach to discovering solutions Choosing the solution representation Devising a random variation operator Determining a rule for solution survival Initialization the population

Solving the traveling salesman problem: 1. Solution representation, 2. Devising random variation operator

Solving the traveling salesman problem: 3. Determining the rule for solution survival, 4. Initialize the population Rule for survival: survival of the fittest— the least total distance traveled. Initial population: in this case, chosen completely at random from the space of possible solutions.

The best result of the 1 st generation for the 100-city traveling salesman problem

The best result of the 500 th generation for the 100-city traveling salesman problem

The best result of the 1000 th generation for the 100-city traveling salesman problem

The best result of the 4000 th generation for the 100-city traveling salesman problem

Drug design using evolutionary algorithm

Evolutionary algorithm in high-level chess game

To probe further What is revolutionary computation, IEEE Spectrum, Feb How to solve It: Modern Heuristics, Zbigniew Michalewicz, Springer, 2000 Evolution, Neural Networks, Games, and Intelligence, Kumar and Fogel, Proceedings of IEEE Vol. 87, no 9, pp , Sept. 1999