Cancer proteomics has become a powerful technique for characterizing the protein markers
driving transformation of malignancy, tracing proteome variation triggered by therapeutics …

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical
data. Recently, deep neural models trained on procedurally-generated synthetic datasets …

Abstract The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based
EA framework that has been shown to perform well in several domains, including Genetic …

Evolution has produced an astonishing diversity of species, each filling a different niche.
Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of …

Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants,
and it has been used with success in several problem domains. Since the original proposal …

Modern genetic programming (GP) operates within the statistical machine learning (SML)
framework. In this framework, evolution needs to balance between approximation of an …

Abstract Denoising Autoencoder Genetic Programming (DAE-GP) is a model-based
evolutionary algorithm that uses denoising autoencoder long short-term memory networks …

Estimation of distribution programming is an iterative method to evolve program trees. It
estimates the distribution of the most suitable program trees and then produces a new …

Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by
aspects of natural evolution. Modern varieties incorporate a broad mixture of search …

Experimental studies confirm that the obtained electrical power by a conventional
photovoltaic PV system is progressively degraded when the temperature of its cells is …