Problem-Solving Strategies Or Heuristics Include

This chapter explores some stochastic local search heuristics that incorporate a data mining procedure.The basic idea of using data mining inside a heuristic is to obtain knowledge from previous iterations performed by a heuristic to guide the search in next iterations.A decoder maps each vector of random keys to a solution program and assigns it a measure of quality.

This chapter explores some stochastic local search heuristics that incorporate a data mining procedure.The basic idea of using data mining inside a heuristic is to obtain knowledge from previous iterations performed by a heuristic to guide the search in next iterations.A decoder maps each vector of random keys to a solution program and assigns it a measure of quality.

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The errors we make because of the use of heuristics and intuition are called cognitive biases.

We immediately recognise the heuristics of trial & error and rules of thumb, and we have a sense of how they are flawed – arguably because less emotion is used in this type of shortcut.

The word heuristics comes from the Greek “find” or “discover” and refers to experience-based techniques for problem solving, learning, and discovery – Wikipedia.

Judea Pearl in ‘Heuristics: Intelligent Search Strategies for Computer Problem Solving’ defines heuristics as strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines.

The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life.

Each chapter of this work includes an abstract/introduction with a short description of the methodology.Experience and loosely applicable information as the basis from which to solve problems immediately sounds potentially flawed.This is even more of a problem when we recognise that emotions play a substantial role in heuristics and intuition.Intuition is closely related to heuristics because intuition can be interpreted as layers of experience and knowledge in an area.The greater your experience of various aspects of the area, and the more niche the area – enabling a concentration of experience – the stronger your intuition can be argued to be.Adaptive metaheuristics (that change their configuration during the search) and multilevel metaheuristics (that change their configuration during the search by means of a metaheuristic) can be a solution for this.This chapter intends to make a quick review of the latest trends in adaptive metaheuristics and in multilevel metaheuristics.This chapter introduces biased random-key genetic programming, a new metaheuristic for evolving programs.Each solution program is encoded as a vector of random keys, where a random key is a real number randomly generated in the continuous interval [0, 1].Also, some results are revisited to demonstrate that even memory-based heuristics can benefit from using data mining by reducing the computational time to achieve good quality solutions.Evolution strategies are classical variants of evolutionary algorithms which are frequently used to heuristically solve optimization problems, in particular in continuous domains.