Manuel Chica is Ramon y Cajal Senior Researcher (RYC-2016-19800), granted by the Spanish Government, at the University of Granada (Computer Science and AI Deparment). He is also Chief AI Officer at ZIO Analytics and Conjoint Lecturer at the University of Newcastle, Australia.
His current research interests include agent-based modeling, evolutionary game theory, single and multi-objective evolutionary optimization, data science, and complex systems. His research is mainly applied and has been applied to a diverse range of fields: from industrial engineering and marketing, to healthcare systems and climate change.
PhD in Artificial Intelligence and Computer Science (cum laude), 2011
University of Granada
Master in Soft Computing and Intelligent Systems, 2009
University of Granada
MEng in Computer Science, 2006
University of Jaen
BSc in Computing Engineering (Management), 2004
University of Jaen
More than 1K (h-index = 19)
Metaheuristics and mixed integer linear models (MILP) for multi-manned assembly line balancing (MALBP)
Ant Colony Optimization (ACO) and Genetic Algorithms to solve the Time and Space Assembly Line Balancing Problem (TSALBP).
The aim of FP7 TOXDTECT project is to develop an intelligent packaging solution for fresh bovine meat products. The technology is based on the identification of specific volatile organic compounds (VOCs) produced inside the packaging during microorganisms’ growth. The signal coming from the interaction between VOCs and polymer inks is recorded by an external device where data are processed. A predictive software based on data mining correlates the sensors signal with the remaining shelf-life of meat (i.e., number of days during which meat is still safe to be consumed).
A decision support system based on computational intelligence and system dynamics for branding and marketing
A European project funded by the European Commission 7th Framework Program. The objective of the Apifresh project is to provide European beekeepers with the scientific and technological aids necessary to improve the quality of European pollen and royal jelly and also to promote the regulatory means that will allow European bee products compete under fair conditions against lower quality or adulterated products.
This FP7 for SMEs project was developed in Inspiralia to create a medical device which automatically recognizes patient’s intentions. Manuel led the intelligent system based on neural networks to identify the patient’s movements
COVID-19 pandemic has impacted millions of people and the global economy and tourism has been one the most affected economic sectors. We propose an evolutionary game model that reflects a collective risk dilemma behind these decisions, given that the dependence on tourism of each region is diverse. Our analysis shows that the existence of heterogeneous costs enhances global agreements. A layout of groups based on similar costs of cooperation boosts the regions’ agreements and avoid the risk of having a total lock-down and a negligible tourism activity
We present a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system, using real-world data of VAT declarations by businesses registered in the Canary Islands, Spain. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. We see increasing the subjective audit probability for low transactions is more efficient than increasing this probability for high transactions. Second, favoring social rewards for cooperators or alternative penalties for defectors can be effective policies, but their success depends on the distribution of the audit probability for low and high transactions.
volume 22 (6) 866-878 Impact factor: 8.508. Journal rank: Q1, 3/104 (Computer Science - Theory & Methods)
volume 54 (5) 752-767 Impact factor: 3.9. Journal rank: Q1, 25/140 (Business)
volume 58 55-68 Impact factor: 4.029. Journal rank: Q1, 2/83 (OR - Management Science)
volume 180 (18) 3465-3487 Impact factor: 2.833. Journal rank: Q1, 9/126 (Computer Science - Intelligent Systems)
volume x y Impact factor: 2.222. Journal rank: Q1, 24/108 (Social Sciences, Int.)
volume 9 55284-55299 Impact factor: 3.745. Journal rank: Q1, 35/156 (C.S. - Information Systems)
volume 59 549-564 Impact factor: 5.105. Journal rank: Q1, 5/83 (Computer Science - Operations Research)
volume 97 (4) 267-285 Impact factor: 1.090. Journal rank: Q4, 82/108 (Computer Science, Software Engineering)
volume 114861 Impact factor: 5.292. Journal rank: Q1, 2/83 (O.R. & Management Science)