Incorporación de conocimiento en algoritmos evolutivos en problemas de scheduling

Los Algoritmos Evolutivos (AEs) son una de las metaheurísticas más ampliamente difundidas y estudiadas [28]. Estas, como muchas otras metaheuristicas, pueden ser mejoradas en su diseño a fin de realizar una exploración más eficiente del espacio de búsqueda. En el caso de los AEs, un adecuado desempeño de los mismos, depende en gran medida de los operadores y/o mecanismos de exploración involucrados y que adecuadamente implementados, pueden dar lugar a versiones más eficientes. En este sentido, la incoporación de conocimiento y/o información en el diseño de los AEs es de gran interés en la actualidad. Por esta razón, existen diversas líneas de investigación en la actualidad que tienen como objetivo principal el diseño avanzado de EAs a través de la incorporación de conocimiento. Esta línea de investigación, aborda diferentes estrategias tales como la incorporación del conocimiento experto a priori o el conocimiento adquirido durante la evolución, conceptos derivados de las teorías de evolución social, y cultural, entre otras.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Incorporación de conocimiento en algoritmos evolutivos en problemas de scheduling

Los Algoritmos Evolutivos (AEs) son una de las metaheurísticas más ampliamente difundidas y estudiadas [28]. Estas, como muchas otras metaheuristicas, pueden ser mejoradas en su diseño a fin de realizar una exploración más eficiente del espacio de búsqueda. En el caso de los AEs, un adecuado desempeño de los mismos, depende en gran medida de los operadores y/o mecanismos de exploración involucrados y que adecuadamente implementados, pueden dar lugar a versiones más eficientes. En este sentido, la incoporación de conocimiento y/o información en el diseño de los AEs es de gran interés en la actualidad. Por esta razón, existen diversas líneas de investigación en la actualidad que tienen como objetivo principal el diseño avanzado de EAs a través de la incorporación de conocimiento. Esta línea de investigación, aborda diferentes estrategias tales como la incorporación del conocimiento experto a priori o el conocimiento adquirido durante la evolución, conceptos derivados de las teorías de evolución social, y cultural, entre otras.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design

Large language models (LLMs) have taken the scientific world by storm, changing the landscape of natural language processing and human-computer interaction. These powerful tools can answer complex questions and, surprisingly, perform challenging creative tasks (e.g., generate code and applications to solve problems, write stories, pieces of music, etc.). In this paper, we present a collaborative game design framework that combines interactive evolution and large language models to simulate the typical human design process. We use the former to exploit users' feedback for selecting the most promising ideas and large language models for a very complex creative task - the recombination and variation of ideas. In our framework, the process starts with a brief and a set of candidate designs, either generated using a language model or proposed by the users. Next, users collaborate on the design process by providing feedback to an interactive genetic algorithm that selects, recombines, and mutates the most promising designs. We evaluated our framework on three game design tasks with human designers who collaborated remotely.Comment: (Submitted

Evolutionary Programming based Recommendation System for Online Shopping

Abstract-In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on eye movement analysis. Given a set of images of different clothes, the eye movement patterns of the human subjects while looking at the clothes they like differ from clothes they do not like. Therefore, in the proposed system, human preference is measured from the way the human subjects look at the images of different clothes. In other words, the human preference can be measured by using the fixation count and the fixation length using an eye tracking system. Based on the level of human preference, the evolutionary programming suggests new clothes that close the human preference by operations such as selection and mutation. The proposed recommendation is tested with several human subjects and the experimental results are demonstrated

Development of colletive intelligence for building energy efficiency

Energy consumption in the building sector is continuously increasing. In response to this situation, optimal collaborative action strategies aimed at improving building energy efficiency with human and building technical systems have become increasingly important. Collaborative actions which this research addresses focus on the interaction between humans and technical systems in a building environment. Most studies on building energy efficiency have dealt with the development of technical systems and lacked consideration of the complex socio-technological interface and collective efforts between technical systems and humans. This research aims to fill the gap by developing an innovative collective intelligence model to enable collective efforts by both building energy systems and people to achieve a greater energy saving. In this model, building energy systems and people are represented by intelligent agents, while genetic algorithms (GAs) are integrated into multi-agent modules to enable self-organization of energy efficient actions in order to achieve optimal energy consumption. The utility of the innovative collective intelligence model is further investigated through a multi-unit apartment building in the Australian context. As an example, the results of the prototype show that building energy performance can be significantly improved by using the proposed collective intelligence model compared to the baseline energy consumption of the building. This research links humans and collective intelligence with building energy systems to tackle energy efficiency problems in the built environment. Research outcomes will advance cross-disciplinary knowledge about the utilisation of artificial intelligence technologies for enhancing energy efficiency and sustainability in the built environment