Special Issue "Evolutionary Machine Learning for Nature-Inspired Problem Solving"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 10 October 2020.

Special Issue Editor

Prof. Dr. Chang Wook Ahn
Website
Guest Editor
AI Graduate School, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
Interests: nature-inspired problem solving; evolutionary machine learning; creativity-model learning intelligence; AI music and arts; quantum AI
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Special Issue Information

Dear Colleagues,

Recently, evolutionary machine learning (EML) has attracted attention due to its enviable success recode in real-world problems in diverse areas; EML is signaling a paradigm shift in machine learning and artificial intelligence research. In some sense, EML has been considered the most promising approach to the next artificial intelligence.  

Conceptually, EML evolves a population of promising solutions/models by following two key principles in biological evolution; natural selection and genetic inheritance, which both emulate some natural processes. These mechanisms simultaneously traverse multiple basins of attraction in a given search space and aptly eliminate noise in the assessment of solutions/models. Owing to its success in the evolutionary process, EML has readily crossed the hurdle of conventional machine learning techniques. In relation to this, many intense research activities in EML have been conducted in recent years.  

The primary aim of this Special Issue is to publish research outcomes related to the theory and design of state-of-the-art EML techniques and innovative applications to nontrivial real-world problems.

Prof. Dr. Chang Wook Ahn
Guest Editor

Manuscript Submission Information

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Keywords

  • Evolutionary algorithms
  • Evolutionary deep learning
  • Evolutionary games/music/arts
  • Evolutionary reinforcement learning
  • Evolving grammars/programs
  • Evolving neural networks
  • Multi-objective optimization
  • Real-world applications
  • Swarm and collective intelligence

Published Papers (2 papers)

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Research

Open AccessArticle
AntsOMG: A Framework Aiming to Automate Creativity and Intelligent Behavior with a Showcase on Cantus Firmus Composition and Style Development
Electronics 2020, 9(8), 1212; https://doi.org/10.3390/electronics9081212 - 28 Jul 2020
Abstract
Creative behavior is one of the most fascinating areas in intelligence. The development of specific styles is the most characteristic feature of creative behavior. All important creators, such as Picasso and Beethoven, have their own distinctive styles that even non-professional art lovers can [...] Read more.
Creative behavior is one of the most fascinating areas in intelligence. The development of specific styles is the most characteristic feature of creative behavior. All important creators, such as Picasso and Beethoven, have their own distinctive styles that even non-professional art lovers can easily recognize. Hence, in the present work, attempting to achieve cantus firmus composition and style development as well as inspired by the behavior of natural ants and the mechanism of ant colony optimization (ACO), this paper firstly proposes a meta-framework, called ants on multiple graphs (AntsOMG), mainly for roughly modeling creation activities and then presents an implementation derived from AntsOMG for composing cantus firmi, one of the essential genres in music. Although the mechanism in ACO is adopted for simulating ant behavior, AntsOMG is not designed as an optimization framework. Implementations can be built upon AntsOMG in order to automate creation behavior and realize autonomous development on different subjects in various disciplines. In particular, an implementation for composing cantus firmi is shown in this paper as a demonstration. Ants walk on multiple graphs to form certain trails that are composed of the interaction among the graph topology, the cost on edges, and the concentration of pheromone. The resultant graphs with the distribution of pheromone can be interpreted as a representation of cantus firmus style developed autonomously. Our obtained results indicate that the proposal has an intriguing effect, because significantly different styles may be autonomously developed from an identical initial configuration in separate runs, and cantus firmi of a certain style can be created in batch simply by using the corresponding outcome. The contribution of this paper is twofold. First, the presented implementation is immediately applicable to the creation of cantus firmi and possibly other music genres with slight modifications. Second, AntsOMG, as a meta-framework, may be employed for other kinds of autonomous development with appropriate implementations. Full article
(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)
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Open AccessArticle
Spellcaster Control Agent in StarCraft II Using Deep Reinforcement Learning
Electronics 2020, 9(6), 996; https://doi.org/10.3390/electronics9060996 - 14 Jun 2020
Abstract
This paper proposes a DRL -based training method for spellcaster units in StarCraft II, one of the most representative Real-Time Strategy (RTS) games. During combat situations in StarCraft II, micro-controlling various combat units is crucial in order to win the game. Among many [...] Read more.
This paper proposes a DRL -based training method for spellcaster units in StarCraft II, one of the most representative Real-Time Strategy (RTS) games. During combat situations in StarCraft II, micro-controlling various combat units is crucial in order to win the game. Among many other combat units, the spellcaster unit is one of the most significant components that greatly influences the combat results. Despite the importance of the spellcaster units in combat, training methods to carefully control spellcasters have not been thoroughly considered in related studies due to the complexity. Therefore, we suggest a training method for spellcaster units in StarCraft II by using the A3C algorithm. The main idea is to train two Protoss spellcaster units under three newly designed minigames, each representing a unique spell usage scenario, to use ‘Force Field’ and ‘Psionic Storm’ effectively. As a result, the trained agents show winning rates of more than 85% in each scenario. We present a new training method for spellcaster units that releases the limitation of StarCraft II AI research. We expect that our training method can be used for training other advanced and tactical units by applying transfer learning in more complex minigame scenarios or full game maps. Full article
(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)
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