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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 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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Investigating multi-view differential evolution for solving constrained engineering design problems

Several constrained and unconstrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In the last decades, different metaheuristics have been proposed employing new ideas, and hybrid algorithms that improve the original metaheuristics have been developed. One of the most successfully employed metaheuristics is the Differential Evolution. In

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Evolutionary algorithms and HP Model for protein structure prediction

Protein structures prediction (PSP) is a computationally complex problem. Simplified models of the protein molecule (such as the HP Model) and the use of evolutionary algorithms (EAs) are among the most investigated techniques for PSP. However, the evaluation of a structure represented by the HP model considers only the number of hydrophobic contacts, which doesn’t

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