A research programme built on one question

How can we design intelligent systems that adapt, optimize and create, not just compute?

My answer is generative optimization: AI that generates, models of engagement that evaluate, and metaheuristics that search, built up since 2012.

Watch the loop run The programmeCV

Behind this page is a live latent space: every point is an embedding, not a word, and each spark is a thought moving through it, attending to its neighbours as it goes. Click anywhere to start one.

/ the loop, running live

Watch an algorithm design an engaging puzzle.

A generator edits Sudoku puzzles, a simulated player rates how engaging each one is, and a search archive keeps the best puzzle for every style. Most designs land too easy; watch the search find the narrow comfortable zone.

GENERATE→EVALUATE→SEARCH↺
CANDIDATE
GAME REFINEMENT · GR = √G / T
too hardcomfortable 0.07–0.08too easy
waiting for the first candidate…
MAP-ELITES ARCHIVE · click a niche
symmetry →
22 givens← fewer clues · more clues →45
comfortabletoo easytoo hard
0
Puzzles evaluated
0 / 40
Niches filled
0
In comfort zone

An illustrative teaching model of my work on puzzle generation and game refinement: the generator here makes random edits where the research uses AI agents, and GR = √G / T is a simplified measure (G cells placed, T candidate values weighed).

/ research programme

Generative optimization: one loop, three stages.

01 · GENERATEGenerative & Agentic AIGenerative models, LLMs and agent teams propose candidate designs.02 · EVALUATEGames Informatics & Engagement ModellingGame refinement theory scores each design by how engaging it is to people.03 · SEARCHComputational Intelligence & OptimizationMetaheuristics and quality-diversity search decide what to generate next.
NOW
Generative Optimization
Closing the loop for individual design problems, optimized against human engagement.
NEXT
Generative Optinformatics
A loop that learns from its own search history and transfers it to new design problems.
Read the programme →

/ selected work

Selected articles.

GENERATE
Khalid, M. N. A. “Simulating AI-Human Collaborative Strategies in Sudoku Puzzle Generation.” In International Conference on Entertainment Computing, 2025.Q3Khalid, M. N. A. and Kamarudin, Nazhatul Hafizah and Virgiyanti, Wiwied and Thurasamy, Ramayah. “Framework for Assisted Knowledge Management Practices Using Large Language Models: Preliminary Findings.” Asia-Pacific Journal of Information Technology and Multimedia 14 (2025). https://doi.org/10.17576/apjitm-2025-1402-05
EVALUATE
Q2Khalid, M. N. A. “Ludogenic innovation: How playing incentivizes technological innovation.” Entertainment Computing 52 (2025): 100900. https://doi.org/10.1016/j.entcom.2024.100900Q1Khalid, M. N. A. and Iida, Hiroyuki. “Objectivity and subjectivity in games: Understanding engagement and addiction mechanism.” IEEE Access 9 (2021): 65187–65205. https://doi.org/10.1109/ACCESS.2021.3075954
SEARCH
Q1Khalid, M. N. A. and Yusof, Umi Kalsom. “Leveraging bio-inspired knowledge-intensive optimization algorithm in the assembly line balancing problem.” IEEE Access 9 (2021): 117832–117844. https://doi.org/10.1109/ACCESS.2021.3106321Khalid, M. N. A. and Yusof, Umi Kalsom. “Incorporating shifting bottleneck identification in assembly line balancing problem using an artificial immune system approach.” Flexible Services and Manufacturing Journal (2021). https://doi.org/10.1007/s10696-020-09389-1
All 113 publications →

Open to collaboration.

PhD and Master's supervision at every stage of the loop, industry research collaboration, and conference invitations.

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