2k citations · 3.4k across the 66 of their papers we have counts for
6 papers · 1 filter
Half a Dozen Real-World Applications of Evolutionary Multitasking, and More
Abhishek Gupta, Lei Zhou, Yew-Soon Ong +2
Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of e…
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…
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…