Titlebook: Design of Modern Heuristics; Principles and Appli Franz Rothlauf Textbook 2011 Springer-Verlag Berlin Heidelberg 2011 Artificial Intelligen

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Optimization Problemse describe the process of how to create a problem model that can be solved by optimization methods, and what can go wrong during this process. Furthermore, we look at important properties of optimization models. The most important one is how difficult it is to find optimal solutions. For some well-s

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Optimization Methodsems. The goal of optimization methods is to find an optimal or near-optimal solution with low computational effort. The effort of an optimization method can be measured as the time (computation time) and space (computer memory) that is consumed by the method. For many optimization methods, and espec

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Search Strategiesn the design and control of the intensification and diversification phases. It is important for search strategies to balance intensification and diversification during search and to allow search methods to escape from local optima. This is achieved by various diversification techniques based on the

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High Locality Representations for Automated Programmingplete programs in an arbitrary language using a variable-length binary string. In contrast to standard GP, which applies search operators directly to phenotypes, GE uses an additional mapping and applies search operators to binary genotypes. Therefore, there is a large semantic gap between genotypes

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Biased Modern Heuristics for the OCST Problem problem we can bias the design elements of modern heuristics, namely the representation and search operator, fitness function, the initial solution, or even the search strategy. This chapter presents a case study on how the performance of modern heuristics can be increased by biasing the design ele

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https://doi.org/10.1007/978-1-4615-4465-4an be applied to a wide range of different problems (they are general-purpose methods) and that use during search both intensification (exploitation) and diversification (exploration) steps. Throughout the book, we learn . We do not study single modern heuristics independently of each other, but pro