Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Frank Neumann, Dirk Sudholt, Carsten Witt
The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the CLIFF function for which it has been shown recently that…
Prasanta Dutta, Anirban Mukhopadhyay
—Compact Genetic Algorithms (cGAs) are condensed variants of classical Genetic Algorithms (GAs) that use a probability vector representation of the population instead of the complete population. cGAs have been shown to significantly reduce the number of function evaluations required while producing outcomes similar to…
Marcel Chwiałkowski, Benjamin Doerr, Martin S. Krejca
—The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA) another simple EDA—, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to…
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
— Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other postprocessing task conducted after…
W. B. Langdon
We summarise how a 3.0GHz 16 core AVX512 computer can interpret the equivalent of up to on average 1 103 370 000 000 GPop/s. Citations to existing publications are given. Implementation stress is placed on both parallel computing, bandwidth limits and avoiding repeated calculation. Information theory suggests in…
Nicola Milano, Stefano Nolfi
The propensity of evolutionary algorithms to generate compact solutions have advantages and disadvantages. On one side, compact solutions can be cheaper, lighter, and faster than less compact ones. On the other hand, compact solutions might lack evolvability, i.e. might have a lower probability to improve as a result…
Sumit Adak, Carsten Witt
Generalized OneMax Authors: ['Sumit Adak' 'Carsten Witt'] Abstract. A class of metaheuristic techniques called estimation-of-distribution algorithms (EDAs) are employed in optimization as more sophisticated substitutes for traditional strategies like evolutionary algorithms. EDAs generally drive the search for the…
Weimin Zheng, Senyuan Pang, Ning Liu, Qingwei Chai + 1 more
Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid…
Tena Škalec, Marko Đurasević, Heming Jia
The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, as…
J. C. Felix-Saul, Mario García-Valdez, Juan J. Merelo Guervós, Oscar Castillo + 1 more
'Oscar Castillo' 'Huiling Chen'] In this paper, we aim to enhance genetic algorithms (GAs) by integrating a dynamic model based on biological life cycles. This study addresses the challenge of maintaining diversity and adaptability in GAs by incorporating stages of birth, growth, reproduction, and death into the…
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…
Bilal Khurshid, Shahid Maqsood, Yahya Khurshid, Khawar Naeem + 1 more
This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing…
Yang Yang, Maosheng Fu, Xiancun Zhou, Chaochuan Jia + 2 more
'Heming Jia'] Intelligent optimization algorithms are crucial for solving complex engineering problems. The Parrot Optimization (PO) algorithm shows potential but has issues like local-optimum trapping and slow convergence. This study presents the Chaotic-Gaussian-Barycenter Parrot Optimization (CGBPO), a modified PO…
Y. Ma, Tan Chye Cheah
problem Authors: ['Y. Ma' 'Tan Chye Cheah'] This paper implements a new way of solving a problem called the traveling salesman problem (TSP) using quantum genetic algorithm (QGA). We compared how well this new approach works to the traditional method known as a classical genetic algorithm (CGA). The TSP is a…