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Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression

Linear Genetic Programming (LGP) is an Evolutionary Computation algorithm, inspired in the Genetic Programming (GP) algorithm. Instead of using the standard tree representation of GP, LGP evolves a linear program, which causes a graph-based data flow with code reuse. LGP has been shown to outperform GP in several problems, including Symbolic Regression (SReg), and to

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Convergence detection for optimization algorithms: Approximate-KKT stopping criterion when Lagrange multipliers are not available

In this paper we investigate how to efficiently apply Approximate-Karush–Kuhn–Tucker proximity measures as stopping criteria for optimization algorithms that do not generate approximations to Lagrange multipliers. We prove that the KKT error measurement tends to zero when approaching a solution and we develop a simple model to compute the KKT error measure requiring only the

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A CMA-ES-based 2-stage memetic framework for solving constrained optimization problems

Constraint optimization problems play a crucial role in many application domains, ranging from engineering design to finance and logistics. Specific techniques are therefore needed to handle complex fitness landscapes characterized by multiple constraints. In the last decades, a number of novel meta-heuristics have been applied to constraint optimization. Among these, the Covariance Matrix Adaptation Evolution

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A modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization

In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the

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Kaizen Programming

This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus

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