Using genetic programming to evolve board evaluation functions

Ferrer, Gabriel J.; Martin, Worthy N.

doi:10.1109/icec.1995.487479

Cited by 8 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On a learning task (rather than maintenance) he shows it gives a performance improvement. [Ferrer and Martin, 1995] also reports improved performance from seeding the initial population with previously found good solutions. While [Kraft et aI., 1994] construct the initial population to contain a high proportion (80% or more) of terminals which the user has chosen as likely to be relevant.…”

Section: Software Maintenancementioning

confidence: 95%

Genetic Programming and Data Structures

Langdon

1998

211

View full text Add to dashboard Cite

Section: Software Maintenancementioning

confidence: 95%

Genetic Programming and Data Structures

Langdon

1998

211

View full text Add to dashboard Cite

“…Another is to evolve the evaluation function alone and combine it with an existing algorithm, e.g., MCTS, instead of evolving fullyfeatured agents. Such an evolution was successfully applied in games of varying complexity, e.g., Checkers [19] and Chess [20].…”

Section: Background a Evolving Evaluation Functionsmentioning

confidence: 99%

Evolving Evaluation Functions for Collectible Card Game AI

Miernik¹,

Kowalski²

2021

Preprint

View full text Add to dashboard Cite

In this work, we presented a study regarding two important aspects of evolving feature-based game evaluation functions: the choice of genome representation and the choice of opponent used to test the model.We compared three representations. One simpler and more limited, based on a vector of weights that are used in a linear combination of predefined game features. And two more complex, based on binary and n-ary trees.On top of this test, we also investigated the influence of fitness defined as a simulation-based function that: plays against a fixed weak opponent, plays against a fixed strong opponent, and plays against the best individual from the previous population.For a testbed, we have chosen a recently popular domain of digital collectible card games. We encoded our experiments in a programming game, Legends of Code and Magic, used in Strategy Card Game AI Competition. However, as the problems stated are of general nature we are convinced that our observations are applicable in the other domains as well.

show abstract

“…Since genetic programming aims at generating an efficient solution expression, it is a useful tool for a wide range of applications, not amenable to most other techniques. Some example applications include machine vision (Chien, Lin & Yang, 2004), games (Ferrer & Martin, 1995;Chen & Yeh, 1996), and virtual reality (Das et al, 1994). Genetic programming is also the primary subject of the annual human-competitive competition, which requires systems to generate solutions which, if they were generated by a human, would be regarded as exhibiting a high level of intelligence and creativity.…”

Section: Genetic Programmingmentioning

confidence: 99%

Nature That Breeds Solutions

Chiong

Neri

McKay

2012

International Journal of Signs and Semiotic Systems

View full text Add to dashboard Cite

Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful techniques, algorithms and computational applications for dealing with large, complex and dynamic real world problems. In this article, the authors discuss why nature-inspired solutions have become increasingly important and favourable for tackling the conventionally-hard problems. They also present the concepts and background of some selected examples from the domain of natural computing, and describe their key applications in business, science and engineering. Finally, the future trends are highlighted to provide a vision for the potential growth of this field.

show abstract

Using genetic programming to evolve board evaluation functions

Cited by 8 publications

References 1 publication

Genetic Programming and Data Structures

Genetic Programming and Data Structures

Evolving Evaluation Functions for Collectible Card Game AI

Nature That Breeds Solutions

Contact Info

Product

Resources

About