24 papers · ranked by Valyu relevance
Mohd Nadhir Ab Wahab, Samia Nefti-Meziani, Adham Atyabi, Catalin Buiu
'Catalin Buiu'] Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known…
Eugene Eberbach
Evolution by natural selection, which is one of the most compelling themes of modern science, brought forth evolutionary algorithms and evolutionary computation, applying mechanisms of evolution in nature to various problems solved by computers. In this paper we concentrate on evolutionary automata that constitute an…
Jean-Baptiste Mouret
Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems. This feat has inspired computer scientists since the advent of computing and led to optimization tools that can evolve complex neural networks for machines—an approach known as ''neuroevolution.'' After a few…
Nazmul Siddique, Hojjat Adeli
This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting…
Mihai Oltean
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem…
Mihai Oltean
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into…
Xin‐She Yang
Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We…
André L. L. Moreira, César Rennó-Costa
Evolution optimizes cellular behavior throughout sequential generations by selecting the successful individual cells in a given context. As gene regulatory networks (GRNs) determine the behavior of single cells by ruling the activation of different processes - such as cell differentiation and death - how GRNs change…
Hassan A. Bashir, R.S. Neville
This material represents the opinion of the author only and does not necessarily represent the opinion of the School or the University. While every attempt has been made to ensure the accuracy of this publication, neither the author, the School or the University can accept liability for mistakes that may exist. Please…
Licheng Jiao, Jiaxuan Zhao, Chao Wang, Xu Liu + 6 more
'Lingling Li' 'Ronghua Shang' 'Yangyang Li' 'Wenping Ma' 'Shuyuan Yang'] Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms.…
Steve O'Hagan, Joshua Knowles, Douglas B. Kell, Josh Bongard
Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this…
Ali Fozooni, Osman Kamari, Mostafa Pourtalebiyan, Masoud Gorgich + 2 more
'Mohammad Khalilzadeh' 'Amin Valizadeh'] In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in…
Olga Speck, Thomas Speck, Sabine Baur, Michael Herdy + 3 more
'Laith Abualigah' 'Xuewen Xia'] With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic…
Daniel Gaissmaier, Matthias van den Borg, Donato Fantauzzi, Timo Jacob
In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in…
Stefano Nolfi, Paolo Pagliuca
We investigate the use of competitive co-evolution for synthesizing progressively better solutions. Specifically, we introduce a set of methods to measure historical and global progress. We discuss the factors that facilitate genuine progress. Finally, we compare the efficacy of four qualitatively different algorithms.…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
Sizhe Yuen, Thomas H. G. Ezard, Adam J. Sobey
Evolutionary computation is a group of biologically inspired algorithms used to solve complex optimization problems. It can be split into evolutionary algorithms, which take inspiration from genetic inheritance, and swarm intelligence algorithms, that take inspiration from cultural inheritance. However, much of the…
Martín Gutiérrez, Yerko Ortiz, Javier Carrión
Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH…
Lillian T. Tatka, Lucian P. Smith, Herbert M. Sauro
Evolutionary algorithms, a class of optimization techniques inspired by biological evolution, have emerged as powerful tools for the optimization of complex systems, including the evolution of mass-action chemical reaction networks. This work explores the application of evolutionary algorithms in this domain…
Authors not listed
Organic molecular crystals offer a broad spectrum of potential applications. The vast number of possible molecules is both an opportunity and a challenge, because of the prohibitive expense of exhaustively searching chemical space to find novel molecules with promising solid-state properties. Computational methods can…
Otman Abdoun, Jâafar Abouchabaka
Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results. Crossover operators play very important role by constructing competitive Genetic Algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Artem Kaznatcheev
Valiant [1] proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype’s fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…