Algorithm design.
Optimisation is only as good as the search engine behind it. I design and benchmark genetic algorithms, evolutionary methods, and hybrid strategies — including custom NDCI-aware fitness functions — and evaluate them across OEM, operator, and MRO perspectives so the chosen algorithm matches the decision context, not the trend cycle.
Watch the population evolve.
A live NSGA-II-style genetic algorithm searching a 3-objective space. Each sphere is an individual; orange spheres are the current Pareto front. Press Run to start evolution.
Non-dominated sorting genetic algorithm. Default workhorse for 2–4 objectives.
Reference-point variant for many-objective problems (5+).
Multi-objective particle swarm — fast on smooth landscapes.
Decomposition-based — strong when objective shape is known.
Bespoke fitness using the Normalised Diagnostic Contribution Index.
Memetic methods for ill-conditioned constraint sets.
Need the right algorithm?
If your team is reaching for whatever's in the closest library, that's a sign you'd benefit from someone who's benchmarked the alternatives in production conditions.
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