Generation of heuristics by transforming the problem representation

This paper formally defines the idea of transforming one problem representation into another. The power of changing the problem representation is demonstrated in the context of heuristic generation. We prove that each problem transformation induces an admissible and monotonic heuristic on the original problem. Furthermore we show that every admissible and monotonic heuristic is induced by some problem transformation. This result generalizes and unifies several approaches for heuristic formation reported on in the literature. We give four techniques for generating problem transformations and we apply these techniques to generate several heuristics found in the literature. We also show that changing the problem representation can prove (automatically) that some problems are unsolvable

Decision-making Tools and Memetic Algorithms in Management and Linear Programming Problems

Operational Research uses a set of tools based on scientific research principles to achieve rational and meaningful management decisions. This article tries to give solution to a highly complex Linear Programming problem by using Simplex method, Solver and a hybrid prototype which combines the theories of Genetic Algorithms with a new local search heuristic technique. Hybridization of these two techniques is becoming known as Memetic Algorithm. Additionally, this article tries to present different techniques to support management decision-making, with the intention of being used increasingly in the business environment sustaining, thus, decisions by mathematics or artificial intelligence and not only by experience.quantitative management; quantitative methods; decision-making; linear programming; operational research; heuristics; hybrid methods; memetic algorithms.

Learning problem solving strategies using refinement and macro generation

In this paper we propose a technique for learning efficient strategies for solving a certain class of problems. The method, RWM, makes use of two separate methods, namely, refinement and macro generation. The former is a method for partitioning a given problem into a sequence of easier subproblems. The latter is for efficiently learning composite moves which are useful in solving the problem. These methods and a system that incorporates them are described in detail. The kind of strategies learned by RWM are based on the GPS problem solving method. Examples of strategies learned for different types of problems are given. RWM has learned good strategies for some problems which are difficult by human standards. © 1990