Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to traditional single-task search, EMT conducts search on multiple tasks simultaneously. It aims improve convergence characteristics across optimization problems at once by seamlessly transferring knowledge among them. Due efficacy EMT, it has attracted lots attentions and several algorithms have been proposed literature. However, existing are usually based a common mode...

Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms biological evolution and behaviors living organisms. In literature, terminology evolutionary algorithms frequently treated same as EC. This article focuses on making survey researches based using ML techniques to enhance EC algorithms. framework an ML-technique enhanced-EC algorithm (MLEC), main idea that has stored ample data about search space, problem features, population information during...

A multifactorial evolutionary algorithm (MFEA) is a recently proposed for multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated capability to outperform its single-task counterpart in terms both convergence speed and solution quality. In MFEA, across realized via crossover between solutions that possess skill factors. This thus essential performance MFEA. However, we note present most...

Recently, evolutionary multitasking (EMT) has been proposed in the field of computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along process across different tasks, performance on each task could be enhanced. The autoencoding-based EMT is recently algorithm. In contrast to most existing algorithms, which conduct knowledge transfer implicitly via crossover, it intends perform explicitly among form solutions, enables...

Abstract Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements developments. A number enhanced GEPs have been proposed to date the real world applications that use them are also multiplying fast. view steadfast growth its importance both academia industry, here review on considered. particular, this paper presents comprehensive progress GEP. The...

Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising approach for evolutionary multi-tasking by automatically exploiting latent synergies between optimization problems, simply through solving them together in an unified representation space [1]. It aims to improve convergence characteristics across multiple problems at once seamlessly transferring knowledge them. In [1], efficacy MFO been studied specific mode transfer form implicit genetic chromosomal...

Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task algorithm (MaTEA), for optimization. the proposed MaTEA, adaptive selection mechanism to select suitable "assisted" task given by considering similarity between tasks and accumulated rewards of knowledge transfer during evolution. Besides, schema via crossover adopted exchange information among improve search efficiency. addition,...

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve single task in one independent run, which inefficient cases where multiple tasks need to be solved at the same time. Recently, multifactorial (MFO) proposed as new paradigm toward multitasking. It intends conduct search on run. To enable multitasking GP, this paper, we propose novel (MFGP) algorithm. best of our...

In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. contrast existing GEP variants, features chromosome representation in which each embedded with subfunctions that can be deployed construct final solution. As part chromosome, are self-learned or self-evolved by algorithm during evolutionary search. By encompassing any partial solution as input arguments another subfunction,...

With the emergence of crowdshipping and sharing economy, vehicle routing problem with occasional drivers (VRPOD) has been recently proposed to involve private vehicles for delivery goods. In this article, we present a generalized variant VRPOD, namely, heterogeneous capacity, time window, driver (VRPHTO), by taking capacity heterogeneity window into consideration. Furthermore, meet requirement in today's cloud computing service, wherein multiple optimization tasks may need be solved at same...

Multifactorial optimization (MFO) is a new paradigm proposed recently for evolutionary multi-tasking. In contrast to traditional approaches, which focus on solving only single problem at time, MFO was solve multiple problems simultaneously. It contended that the concept of multi-tasking provides scope implicit knowledge transfer useful traits across different but related domains, thereby enhancing search problem-solving. With aim multi-tasking, multifactorial algorithm (MFEA) in [1], and...

This paper presents the comparison of performance on a simple genetic algorithm (SGA) using roulette wheel selection and tournament selection. A SGA is mainly composed three operations, which are selection, crossover mutation. With same mutation operation, simulation results studied by comparing different strategies discussed in this paper. Qualitative analysis depicted, numerical experiments show that with strategy converges much faster than

Railway timetable scheduling is a fundamental operational problem in the railway industry and has significant influence on quality of service provided by transport system. This paper explores periodic (PRTS) problem, with objective to minimize average waiting time transfer passengers. Unlike traditional PRTS models that only involve lines fixed cycles, this presents more flexible model allowing cycle number passengers vary period. An enhanced differential evolution (DE) algorithm dual...

Multi-task optimization is a hot research topic in the field of evolutionary computation. This paper proposes an efficient surrogate-assisted multi-task framework (named SaEF-AKT) with adaptive knowledge transfer for optimization. In proposed SaEF-AKT, several tasks which are computationally expensive solved jointly each generation. Surrogate models built based on historical search information task to reduce number fitness evaluations. To improve efficiency, general similarity measure...

The increasing population density in public places necessitates urgent attention to address safety concerns via effective crowd management. In many congested scenarios such as peak-hour subway stations, the utilization of fences guide movement has become a widely adopted approach alleviate congestion. This work presents method that combines simulation and management, focusing on optimization fence layout for efficient guidance. First, congestion probability social force model (CP-SFM) is...

Genetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on single form to solve the problem, which limits their search diversity increases likelihood of getting stuck in local optima, especially complex scenarios. In this paper, we propose general multiform framework improve performance complicated SR As far as know, paper first attempt integrate optimization paradigm with accelerate performance. The key...

OBJECTIVE Endovascular treatment (EVT) is an effective for patients with acute vertebrobasilar artery complex occlusion (VBAO). However, the benefit of bridging thrombolysis prior to EVT remains controversial. The purpose present study explore best strategy between (BT) and direct in VBAO. METHODS Patients VBAO who underwent within 24 hours estimated a nationwide retrospective registry at 65 stroke centers 15 provinces China from December 2015 June 2022 were retrospectively analyzed....

Arhopalus unicolor is a carrier of the pine wood nematode (PWN), which causes wilt disease, killing trees and causing considerable economic environmental losses. While A. mitochondrial genome has been published, high-quality assembly annotation not yet available. To address this, we assembled chromosome-level reference with combination Illumina, PacBio, Hi-C sequencing technologies. The final size was determined to be 1268.11 Mb, GC% 32.44%, scaffold N50 value 19.30 Mb. A total 98.77%...