Populationsearch

• Utilization of Active Inference framework for anticipatory adaptation.
• Experimental evaluation of Ant Colony Optimization with Active Inference.
• Analysis of performance based on varying graph structures.
• Comparison of traditional reactive approaches with the proposed anticipatory model.

Key Findings

• Active Inference can enhance the performance of population-based metaheuristics.
• Improved solutions were achieved with only a slight increase in computational resources.
• Performance patterns were observed that depend on the number and topology of nodes in the graph.
• The study suggests directions for future research on Active Inference in optimization contexts.

Prerequisites

• Probability theory and Bayesian inference basics
• Free Energy Principle and generative models

🎯 Consulting & Tutoring

Daniel Ari Friedman, PhD is available for AI Research Consulting and Tutoring related to this skill.

Related Skills

See BIBLIOGRAPHY.md for the complete publication catalog and related papers.