Population heuristics

in "Handbook of Applied Optimization", Pardalos, P.M. and Resende, M.G.C. eds (Oxford University Press, Oxford) 2002, 138-157

Standard heuristics in Operations Research (such as greedy, tabu search and simulated annealing) work on improving a single current solution. Population heuristics use a number of current solutions and combine them together to generate new solutions.

Heuristic algorithms encountered in the literature that can generically be classified as population heuristics include genetic algorithms, hybrid genetic algorithms, memetic algorithms, scatter search algorithms and bionomic algorithms.

In this article we discuss the basic features of population heuristics and provide practical advice about their effective use for solving Operations Research problems.

J E Beasley