Search · four archives
Search · four archives
15 papers · ranked by Valyu relevance
Yuhang Xie, Wei Li, Cheng Zhong, Shang Gao + 4 more
Given the growing complexity of continuous optimization problems in strongly coupled and black-box environments, this study proposes a novel adaptive gradient-guided metaheuristic, referred to as Self-Adaptive AdamW-Guided Optimization (SAWG). Without requiring explicit gradient information, SAWG constructs…
Iztok Fister Jr., Simon Fong, Janez Brest, Iztok Fister
Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising…
Szilárd Kovács, Csaba Budai, János Botzheim
In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends the Bacterial Memetic Algorithm by integrating Cultural Algorithms and co-evolutionary dynamics inspired by bacterial group behavior. This combination of…
Meijun Duan, Hongyu Yang, Shangping Wang, Yu Liu + 1 more
'Mohd Nadhir Ab Wahab'] Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy…
Tae Jong Choi, Chang Wook Ahn, Jinung An
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are…
Alok Kumar Shukla, Shubhra Dwivedi, Deepak Singh, Sunil Kumar Singh + 2 more
'Diwakar Tripathi' 'Ram Kishan Dewangan'] Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital…
Yao Yao, Kathleen Marchal, Yves Van de Peer, Vladimir Brezina
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a…
Qun Song, Simon Fong, Suash Deb, Thomas Hanne
Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and demonstrated its capability especially for large-scale problems.…
Emanuel Vega, José Lemus-Romani, Ricardo Soto, Broderick Crawford + 4 more
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years…
Motoaki Hiraga, Masahiro Komura, Akiharu Miyamoto, Daichi Morimoto + 2 more
'Kazuhiro Ohkura' 'Ziqiang Zeng'] Neuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights of neural networks. In…
Brindha Subburaj, S. Miruna Joe Amali
Simulated by nature’s evolution, numerous evolutionary algorithms had been proposed. These algorithms perform better for a particular problem domain and extensive parameter fine tuning and adaptations are required in optimizing problems of varied domain. This paper aims to develop robust and self-adaptive memetic…
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…
Moshe Sipper, Weixuan Fu, Karuna Ahuja, Jason H. Moore
Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm…
Kejia Liu, Yiping Teng, Fang Liu, Ziqiang Zeng
The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handling high-dimensional…
David A Winkler, Leroy Cronin
A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex…