![]() ![]() Multiple linear regression and naive models are also suggested as baseline for comparison with the various techniques. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors are proposed and explained. A concise review of key articles that presented comparisons among various DDM techniques is presented. The most important concerns regarding the way data driven modeling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. In this first part, an extensive data-driven modeling experiment is proposed. A comprehensive data driven modeling experiment is presented in a two-part paper. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.read more read lessĪbstract. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.read more read lessĪbstract: It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. This case study requires the concurrent optimization of several heterogeneous aspects. A case study of a complex real-world optimization problem from the automotive domain is introduced. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. The proposed concept is implemented as open source reference OPT4J. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. Abstract: This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately.
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