Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al
Symbolic regression (SR) is one of the most powerful machine learning techniques that produces transparent models, searching the space of mathematical expressions for a model that represents the relationship between the predictors and the dependent variable without the need of taking assumptions about the model structure. Currently, the most prevalent learning algorithms for SR are based on genetic programming (GP), an evolutionary algorithm inspired from the well-known principles of natural selection. This book is an in-depth guide to GP for SR, discussing its advanced techniques, as well as examples of applications in science and engineering. The basic idea of GP is to evolve a population of solution candidates in an iterative, generational manner, by repeated application of selection, crossover, mutation, and replacement, thus allowing the model structure, coefficients, and input variables to be searched simultaneously. Given that explainability and interpretability are key elements for integrating humans into the loop of learning in AI, increasing the capacity for data scientists to understand internal algorithmic processes and their resultant models has beneficial implications for the learning process as a whole. This book represents a practical guide for industry professionals and students across a range of disciplines, particularly data science, engineering, and applied mathematics. Focused on state-of-the-art SR methods and providing ready-to-use recipes, this book is especially appealing to those working with empirical or semi-analytical models in science and engineering.
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al
The main objective of logistics is to co-ordinate the movement of products through the supply chain in a way that meets customer requirements at minimum cost. In the past this cost has been defined in purely monetary terms. As concern for the environment rises, companies must take more account of the external costs of logistics associated mainly with climate change, air pollution, noise, vibration and accidents. Green Logistics analyses the environmental consequences of logistics and how to deal with them. Written by a leading team of logistics academics, the book examines ways of reducing these externalities and achieving a more sustainable balance between economic, environmental and social objectives.
In many decision problems, e.g. from the area of production and logistics manage ment, the evaluation of alternatives and the determination of an optimal or at least suboptimal solution is an important but dif?cult task. For most such problems no ef?cient algorithm is known and classical approaches of Operations Research like Mixed Integer Linear Programming or Dynamic Pro gramming are often of limited use due to excessive computation time. Therefore, dedicated heuristic solution approaches have been developed which aim at providing good solutions in reasonable time for a given problem. However, such methods have two major drawbacks: First, they are tailored to a speci?c prob lem and their adaption to other problems is dif?cult and in many cases even impos sible. Second, they are typically designed to “build” one single solution in the most effective way, whereas most decision problems have a vast number of feasible solu tions. Hence usually the chances are high that there exist better ones. To overcome these limitations, problem independent search strategies, in particular metaheuris tics, have been proposed. This book provides an elementary step by step introduction to metaheuristics focusing on the search concepts they are based on. The ?rst part demonstrates un derlying concepts of search strategies using a simple example optimization problem.
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