367 citations · 1.3k across the 62 of their papers we have counts for
13 papers · 1 filter
Insights from Multi-tasking the EAX Algorithm for the Travelling Salesperson Problem
Liam Wigney, Aneta Neumann, Yew-Soon Ong +1
Evolutionary multitasking allows several related problems to be solved in a single run of an algorithm. In this paper, we investigate integrating evolutionary multitasking with Edg…
Novelty Search with Cross-Task Collaborative Discovery
Jiao Liu, Yanchi Li, Hua Yu +2
Novelty Search (NS) promotes exploration by rewarding behaviorally novel solutions rather than directly optimizing predefined objectives, making it particularly useful when objecti…
MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization
Shengcai Liu, Zhiyuan Wang, Yew-Soon Ong +2
Discrete optimization is ubiquitous in science and engineering. The vast array of existing discrete optimization problems, coupled with the continuous emergence of new ones, necess…
Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization
Wenjie Chen, Shengcai Liu, Yew-Soon Ong +2
Real-time solutions to the influence blocking maximization (IBM) problems are crucial for promptly containing the spread of misinformation. However, achieving this goal is non-triv…
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
Xuanfeng Li, Shengcai Liu, Jin Wang +3
The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant real-world applications, this work investigate…
Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes
Nick Zhang, Abhishek Gupta, Zefeng Chen +1
Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity pers…
Can Transfer Neuroevolution Tractably Solve Your Differential Equations?
Jian Cheng Wong, Abhishek Gupta, Yew-Soon Ong
This paper introduces neuroevolution for solving differential equations. The solution is obtained through optimizing a deep neural network whose loss function is defined by the res…
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
Mojtaba Shakeri, Erfan Miahi, Abhishek Gupta +1
In today's digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing…
Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution
Adam Żychowski, Abhishek Gupta, Jacek Mańdziuk +1
This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs)…
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
Yuan Yuan, Yew-Soon Ong, Liang Feng +7
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be s…
Co-evolutionary multi-task learning for dynamic time series prediction
Rohitash Chandra, Yew-Soon Ong, Chi-Keong Goh
Time series prediction typically consists of a data reconstruction phase where the time series is broken into overlapping windows known as the timespan. The size of the timespan ca…
Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
Abhishek Gupta, Yew-Soon Ong
Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to…
Meme as Building Block for Evolutionary Optimization of Problem Instances
Liang Feng, Yew Soon Ong, Ah Hwee Tan +1
A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Partic…