Proposer
Prof. Saúl Zapotecas-Matínez, Diego Oliva and Abel García Nájera
Workshop Code
Please use the following code when submitting your paper to this Workshop: W9-SWARM
Abstract
Swarm Intelligence and Evolutionary Computation are two critical areas within Artificial Intelligence that focus on solving optimization problems. Swarm Intelligence draws inspiration from the collective behaviors observed in nature, such as bird flocks or fish schools. This approach has led to algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), which solve problems by leveraging simple interactions among individuals. On the other hand, Evolutionary Computation mimics the principles of natural selection and genetics by evolving populations of candidate solutions through mechanisms such as selection, crossover, and mutation. Popular algorithms in this category include Genetic Algorithms (GA) and Evolution Strategies (ES). Both methodologies are widely applied across fields like optimization, machine learning, and robotics, where they explore complex solution spaces by leveraging nature-inspired processes.
Scope and Aims
The workshop will explore the dynamic field of Swarm Intelligence and Evolutionary Computation, focusing on their theoretical foundations, recent algorithmic advancements, and practical applications. A broad range of topics will be discussed, including but not limited to:
- Recent algorithm developments include PSO, ACO, GA, and ES. New methods for addressing complex optimization problems, including large-scale, many-objective, constrained, and mixed-variable challenges.
- Applying swarm and evolutionary techniques in robotics, machine learning, optimization, and bioinformatics fields.
- Interdisciplinary approaches that integrate these computational techniques with other scientific fields.
- Current challenges and future research directions in Swarm Intelligence and Evolutionary Computation.
Given the diverse range of topics in swarm and evolutionary algorithms, the proposed workshop aims to:
- Knowledge Dissemination: Presenting the latest research and advancements in
Swarm Intelligence and Evolutionary Computation, offering insights into both
fundamental principles and innovative applications. - Facilitating Collaboration: Creating opportunities for networking and collaboration
among researchers, practitioners, and students to foster innovation and joint projects. - Exploring Challenges: Identifying and addressing current challenges in the field to
foster new research directions and solutions. - Promoting Interdisciplinary Research: Encouraging the integration of swarm and
evolutionary methods with other scientific disciplines to solve complex, real-world
problems.
Content and Objectives
The workshop will cover various topics and activities, focusing on examining the latest advancements in Swarm Intelligence and Evolutionary Computation. The program will feature:
- Keynote Presentations: Renowned experts will present cutting-edge research and developments in Swarm Intelligence and Evolutionary Computation, covering theoretical foundations and practical applications.
- Technical Sessions: Oral presentations and discussions will highlight recent findings in Swarm Intelligence and Evolutionary Computation. These presentations are based on accepted papers, which are categorized into two submission types: regular papers (6 pages) and short papers (2 pages), both of which include figures, tables, and references.
- Poster Sessions: Opportunities for participants to present their research in a more
informal setting, encouraging feedback and collaboration.
In this regard, the primary objectives of the workshop are to:
- Disseminate Knowledge: Share the latest research and innovations, enhancing participants’ understanding of Swarm Intelligence and Evolutionary Computation.
- Foster Collaboration: Promote networking and collaboration among the artificial intelligence community.
- Enhance Skills: Equip participants with practical skills through workshops and tutorials regarding Swarm Intelligence and Evolutionary Computation.
- Address Challenges: Encourage the exploration of current challenges in the field, promoting innovative solutions.
- Support Interdisciplinary Research: Advocate for applying these methods across diverse scientific and engineering fields.