Research

Evolutionary Computation

Evolutionary computation refers to a collective term for optimization methods inspired by the mechanisms of biological evolution. Representative methods include genetic algorithms, genetic programming, and evolutionary strategies. Organisms in the natural world evolve by undergoing survival selection and elimination across generations to adapt to their environment. In evolutionary computation, this mechanism of environmental adaptation is modeled on computers to discover individuals—optimal solutions—adapted to specific environments in the context of optimization problems.

Our research lab focuses on addressing the issue of time escalation in evaluating solutions within the optimization process of evolutionary computation. Specifically, we are exploring approaches to resolve this issue, notably through parallel evolutionary computation and surrogate-based evolutionary computation.

Surrogate-based Evolutionary Computation

One challenge in evolutionary computation arises from the substantial number of evaluations required during the optimization process, leading to increased execution time. To address this problem, we are researching surrogate-based evolutionary computation, where machine learning models estimate solution evaluation values while optimizing.

Parallel Evolutionary Computation

To enhance the computational efficiency and explorative performance of evolutionary computation, we are studying parallel evolutionary computation, executing evolutionary computations across multiple machines simultaneously. Specifically, we’re researching methods such as reducing waiting times on parallel computers through semi-asynchronous parallel evolutionary computation and proposing parent selection strategies considering the frequency of exploration to resolve issues where the explorative performance of parallel evolutionary computation decreases when there’s heterogeneity in evaluation times for individual solutions.

Machine Learning

Cooperative Autonomous Driving using Reinforcement Learning

Envisioning the future prevalence of autonomous vehicles, we’ve devised a method to acquire appropriate control strategies for situations where only autonomous vehicles navigate roads. This method is achieved through reinforcement learning. We’ve confirmed that using only local information obtained from distance sensors and vehicle-to-vehicle communication, vehicles with different speeds can navigate roads without collisions.