34 citations · 94 across the 29 of their papers we have counts for
7 papers · 1 filter
Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
Jacob Schrum, Jake Gutierrez, Vanessa Volz +3
Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algor…
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
Simon M. Lucas, Vanessa Volz
This paper provides a detailed investigation of using the Kullback-Leibler (KL) Divergence as a way to compare and analyse game-levels, and hence to use the measure as the objectiv…
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…