Search Results for author: Erik Goodman

Found 12 papers, 4 papers with code

An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems

no code implementations18 Sep 2022 Abhiroop Ghosh, Kalyanmoy Deb, Erik Goodman, Ronald Averill

User knowledge can be formulated as inter-variable relationships to assist an optimization algorithm in finding good solutions faster.

Enhanced Innovized Repair Operator for Evolutionary Multi- and Many-objective Optimization

no code implementations21 Nov 2020 Sukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb, Erik Goodman

"Innovization" is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems.

It is Time for New Perspectives on How to Fight Bloat in GP

no code implementations1 May 2020 Francisco Fernández de Vega, Gustavo Olague, Francisco Chávez, Daniel Lanza, Wolfgang Banzhaf, Erik Goodman

This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested.

Distributed Computing Evolutionary Algorithms

Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

1 code implementation3 Dec 2019 Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, Vishnu Naresh Boddeti

While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.

Classification Computational Efficiency +4

Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems

no code implementations16 Dec 2018 Zhun Fan, Wenji Li, Zhaojun Wang, Yutong Yuan, Fuzan Sun, Zhi Yang, Jie Ruan, Zhaocheng Li, Erik Goodman

In the top sub-population, the search process is divided into two different stages --- push and pull stages. An adaptive DE variant with three trial vector generation strategies is employed in the proposed PPS-DE.

MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems

no code implementations10 Feb 2018 Zhun Fan, Yi Fang, Wenji Li, Xinye Cai, Caimin Wei, Erik Goodman

The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.

An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions

no code implementations27 Jul 2017 Zhun Fan, Wenji Li, Xinye Cai, Han Huang, Yi Fang, Yugen You, Jiajie Mo, Caimin Wei, Erik Goodman

In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs.

A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems

no code implementations1 Apr 2015 Zhun Fan, Wenji Li, Xinye Cai, Huibiao Lin, Shuxiang Xie, Erik Goodman

In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them.

Evolutionary Algorithms

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