4.5 Article

A user-guided innovization-based evolutionary algorithm framework for practical multi-objective optimization problems

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Using semi-independent variables to enhance optimization search

Amir H. Gandomi et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Management

A population-based fast algorithm for a billion-dimensional resource allocation problem with integer variables

Kalyanmoy Deb et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2017)

Proceedings Paper Computer Science, Theory & Methods

Adaptive Use of Innovization Principles for a Faster Convergence of Evolutionary Multi-Objective Optimization Algorithms

Abhinav Gaur et al.

PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION) (2016)

Article Operations Research & Management Science

Higher and lower-level knowledge discovery from Pareto-optimal sets

Sunith Bandaru et al.

JOURNAL OF GLOBAL OPTIMIZATION (2013)

Article Computer Science, Interdisciplinary Applications

Aerodynamic optimization via multi-objective micro-genetic algorithm with range adaptation, knowledge-based reinitialization, crowding and epsilon-dominance

Andras Szollos et al.

ADVANCES IN ENGINEERING SOFTWARE (2009)

Article Computer Science, Artificial Intelligence

A fast and elitist multiobjective genetic algorithm: NSGA-II

K Deb et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2002)