18 citations · 82 across the 21 of their papers we have counts for
5 papers · 1 filter
Evolving Constrained Reinforcement Learning Policy
Chengpeng Hu, Jiyuan Pei, Jialin Liu +1
Evolutionary algorithms have been used to evolve a population of actors to generate diverse experiences for training reinforcement learning agents, which helps to tackle the tempor…
The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation
Simon M Lucas, Jialin Liu, Diego Perez-Liebana
This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems.…
Efficient Noisy Optimisation with the Sliding Window Compact Genetic Algorithm
Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
The compact genetic algorithm is an Estimation of Distribution Algorithm for binary optimisation problems. Unlike the standard Genetic Algorithm, no cross-over or mutation is invol…
Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various…
Optimal resampling for the noisy OneMax problem
Jialin Liu, Michael Fairbank, Diego Pérez-Liébana +1
The OneMax problem is a standard benchmark optimisation problem for a binary search space. Recent work on applying a Bandit-Based Random Mutation Hill-Climbing algorithm to the noi…