Prof Joshua Knowles

Honorary Professor

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Research interests

My broad areas of interest are in artificial intelligence, evolutionary computation, global optimization, multiobjective optimization and computational biology.

A selected list of more fine-grained research topics are these:

  • multiobjective evolutionary algorithms
  • performance assessment of multiobjective optimizers
  • No-free-lunch theorems in multiobjective optimization
  • archiving algorithms (online) in multiobjective optimization
  • closed-loop optimization (in which an optimizer interacts with a live experimental system)
  • memetic algorithms and hybrid evolutionary computation, including hybrids with exact optimizers
  • swarm intelligence algorithms, including ant colony optimization and ant-based clustering
  • "multiobjectivization" - solving single objective problems by introducing additional objectives
  • using surrogate models in optimization, and optimization of expensive black-box problems
  • solving many-objective optimization problems (i.e., problems with more than 3 objectives)
  • dynamic constraints in optimization - "ephemeral resource constraints"
  • co-operative and competitive multi-agent / coevolution approaches to optimization
  • reinforcement learning in optimization
  • multiobjective machine learning, including multiobjective clustering and model selection
  • understanding fitness landscapes and their effects on evolution
  • ab initio protein structure prediction
  • neutral evolution and evolution of evolvability
  • machine learning in radio pulsar discovery
  • game theory models and evolution on networks
  • optimization in chemical engineering
  • directed evolution of DNA aptamers on micro-arrays
  • directed evolution of drug combinations (drug discovery)
  • evolutionary algorithms in chemometrics





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