Interactive multi-criterion decision-making (iM-CDM) procedures typically require a human decision-maker (DM) to iteratively compute a preferred Pareto-optimal solution by incorporating her/his preference information. Many iMCDM procedures involve classifying the objectives according to whether they need to be improved...
Deepanshu Yadav, Kalyanmoy Deb· 2026 International Conferenc...· 0 citations
EvE (Evolutionary Explorer), a steady-state, population-of-four differential evolution (DE) optimizer with a targeted Adam fallback: each iteration proposes one candidate via DE, running a short burst of gradient descent only if the DE step fails to improve on the incumbent.
This paper proposes a machine-based decision-maker (Machine-DM) in terms of pre-trained machine learning (ML) models to provide decision-making information and describes the development process in detail and demonstrates its working through a specific iMCDM procedure – NIMBUS.
Deepanshu Yadav, Kalyanmoy Deb· ACM Transactions on Evolutio...· 0 citations
This paper proposes a novel framework: Ensemble Surrogate-assisted hybrid search with importance-aware Dimensional Splitting (ESDS), which dynamically allocates the dimensional budget to the most critical variables via data-driven sensitivity analysis.
Jun-Feng Yi, Shu-Wei Zhu, Meiji Cui et al.· GECCO Companion· 0 citations
This work studies how to make the diverse trade-off architectures to possess a regularity, so they can be better understood, maintained, and deployed with confidence, and shows that this regularity-driven search can produce families of architectures that remain competitive in performance while being structurally simple...
This paper proposes a stochastic machine learning-based decision-maker: pMachine-DM, which emulates human preference articulation using two stochastic ANNs, enabling systematic and reproducible benchmarking without involving human DMs.
A Machine-based Decision Maker that replaces human DMs with pre-trained machine learning models capable of performing the key iMCDM tasks automatically and a Bench-iMCDM framework for benchmarking iMCDM procedures is proposed.
Deepanshu Yadav, Kalyanmoy Deb· Annual Conference on Genetic...· 2 citations
The method combines an interpretable cost-function formulation with a multi-objective evolutionary algorithm to discover Pareto-optimal ensemble configurations and introduces a two-stage combiner that produces diverse candidate responses and selects final outputs via embedding-based semantic consensus.
