15 papers · ranked by Valyu relevance
Amala Mary Vincent, P. Jidesh
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image…
Zoran Jakšić, Swagata Devi, Olga Jakšić, Koushik Guha + 3 more
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at…
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.…
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Hoda Zamani, Seyedali Mirjalili + 2 more
'Seyedali Mirjalili' 'Mohamed Abd Elaziz' 'Diego Oliva'] Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a…
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…
Alexander V. Spirov, Ekaterina M. Myasnikova, Josh Bongard
Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches…
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…
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…
Boris Almonacid, Ying Tan, Liangjun Ke
Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can find a solution in a reasonable amount of time. However, the time…
Csenge Petak, Lapo Frati, Renske M. A. Vroomans, Melissa H. Pespeni + 1 more
Title: Significance The speed and direction of evolution depend on the availability of phenotypic variation. Genotype-to-phenotype maps can, over time, bias phenotypic variability to more readily produce alternative adaptive phenotypes in fluctuating environments. However, because properties of the fitness landscapes…
Innocent Sibanda, Geoff Nitschke
The goal of bioengineering in synthetic biology is to redesign, reprogram, and rewire biological systems for specific applications using standardized parts such as promoters and ribosomes. For example, bioengineered micro-organisms capable of cleaning up environmental pollution or producing antibodies de novo to defend…
Alexander Lalejini, Emily Dolson, Anya E Vostinar, Luis Zaman + 2 more
'C Brandon Ogbunugafor' 'Christian R Landry'] Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses…
Rui Zhong, Fei Peng, Enzhi Zhang, Jun Yu + 4 more
'Heming Jia' 'Laith Abualigah' 'Xuewen Xia'] We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a…
Maxim Sakharov, Heming Jia
Memetic algorithms achieve strong optimization performance by combining population-based global search with local refinement operators, yet their effectiveness critically depends on the design and management of memes. Local search strategies are typically handcrafted, problem-specific, and fixed prior to execution.…
Ahmed Redha Mahlous, Houssam Mahlous, Yilun Shang
Universities face a constant challenge when distributing students and allocating them to their required classes, especially for a large mass of students. Generating feasible timetables is a strenuous task that requires plenty of resources, which makes it impractical to take student preferences into consideration during…