iBOA: The Incremental Bayesian Optimization Algorithm
Martin Pelikan, Kumara Sastry, and David E. Goldberg

TL;DR
The paper introduces iBOA, an incremental Bayesian optimization algorithm that learns and updates Bayesian networks without a population, enabling scalable and reliable optimization of complex problems.
Contribution
It presents a novel incremental approach to Bayesian optimization that updates network structure and parameters without relying on a solution population.
Findings
iBOA can learn unrestricted Bayesian networks incrementally
It effectively solves difficult nearly decomposable problems
Demonstrates scalability and reliability in optimization tasks
Abstract
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · AI-based Problem Solving and Planning · Machine Learning and Algorithms
