CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
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QUIVER adaptively mixes objective evaluations with two types of preference queries in surrogate-assisted evolutionary multi-objective optimization to reduce final utility regret, reporting 25% gains on hard WFG benchmarks.
A tool combining full hydrodynamic modeling with a bespoke evolutionary algorithm to optimize blue-green infrastructure for reducing urban flood vulnerability at property scale.
CHAMB-GA provides a microservice architecture with containers and a message broker to decouple genetic operations from fitness evaluations, enabling consistent scaling from small machines to over 3500 CPU cores on cloud and HPC systems for optimization problems.
citing papers explorer
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Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
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QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization
QUIVER adaptively mixes objective evaluations with two types of preference queries in surrogate-assisted evolutionary multi-objective optimization to reduce final utility regret, reporting 25% gains on hard WFG benchmarks.
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Optimising Urban Flood Resilience
A tool combining full hydrodynamic modeling with a bespoke evolutionary algorithm to optimize blue-green infrastructure for reducing urban flood vulnerability at property scale.
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CHAMB-GA: A Containerized HPC Scalable Microservice-Based Framework for Genetic Algorithms
CHAMB-GA provides a microservice architecture with containers and a message broker to decouple genetic operations from fitness evaluations, enabling consistent scaling from small machines to over 3500 CPU cores on cloud and HPC systems for optimization problems.