Prof. Pietro S. Oliveto

Southern University of Science and Technology, China

Speech Title: Computational Complexity Analysis of Sexual Evolution for the Design of Better General Purpose Algorithms for AI

Abstract: Large classes of the general-purpose optimisation algorithms at the heart of modern artificial intelligence and machine learning technologies are inspired by models of Darwinian evolution. In this talk we show how the foundational computational complexity analysis of such algorithms leads to an understanding of their behaviour and performance. Such understanding in turn allows informed decisions on how to set their many parameters and how to improve the algorithms to allow for the obtainment of better solutions in shorter time. We provide two concrete examples of how such analyses can lead to counter intuitive insights into how to design sexual evolution inspired algorithms (using populations and recombination) and how to set their parameters such that they can considerably outperform their single trajectory and mutation only (asexual) counterparts at hillclimbing unimodal functions, and at escaping from local optima. We conclude the talk by presenting experimental results that confirm the superiority of the designed algorithms that was proven for benchmark functions with significant structures, for classical combinatorial optimisation problems with practical applications.

Biography: Pietro S. Oliveto is a Professor of Computer Science at the Southern University of Science and Technology (SUSTech) Shenzhen, China. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. He has been EPSRC PhD+ Fellow (2009-2010) and EPSRC Postdoctoral Fellow (2010-2013) at the University of Birmingham, UK and Vice-Chancellor's Fellow (2013-2016) and EPSRC Early Career Fellow (2015-2020) at the University of Sheffield, UK. Before moving to SUSTech he was Chair in Algorithms at the Department of Computer Science, University of Sheffield, UK.
His main research interest is the performance analysis, in particular the time complexity, of bio-inspired computation techniques including evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics and algorithm configurators. He is currently building a Theory of Artificial Intelligence Lab at SUSTech.
He has guest-edited journal special issues of Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science, IEEE Transactions on Evolutionary Computation and Algorithmica. He has co-Chaired the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and has been co-program Chair of the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021) and Theory Track co-chair at ACM GECCO 2022, ACM GECCO 2023 and ACM GECCO 2026. He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), and was Leader of the Benchmarking Working Group of the EU-COST Action ImAppNIO, member of the EPSRC Peer Review College and recently completed his term as Associate Editor of IEEE Transactions on Evolutionary Computation.

Prof. Eugene Rex Jalao

University of the Philippines, Philippines

Speech Title: AI-Enabled Measurement and Shelf Space Assignment Optimization in Retail Systems

Abstract: Shelf space allocation in retail environments is commonly formulated as a constrained optimization problem, where accurate estimates of product dimensions and shelf capacities are critical inputs. In practice, these parameters are often obtained through manual measurement processes that are time-intensive and prone to variability, thereby limiting the effectiveness of optimization-based decision models.

This paper proposes an integrated framework that combines automated measurement and shelf space assignment optimization. A computer vision–based measurement module, utilizing fiducial markers (ArUco), is developed to estimate product dimensions and shelf occupancy. These estimates are incorporated into a binary integer programming model that determines optimal product-to-shelf assignments under capacity and placement constraints, with the objective of maximizing profit- or demand-weighted utility.

To evaluate the operational and computational impact of the proposed approach, a discrete-event simulation model is constructed to replicate the measurement and allocation processes under both manual and AI-assisted scenarios. The simulation framework captures stochastic processing times and measurement deviations, enabling a comparative assessment of system performance in terms of efficiency and solution quality.

Empirical results from case studies in Philippine retail settings indicate that the proposed system reduces measurement time by 41.24% in a supermarket environment and by 58.33% in a hypermarket setting, while simultaneously improving parameter reliability for optimization. The integration of automated measurement with mathematical programming leads to more consistent and scalable shelf allocation decisions.

The findings highlight the importance of accurate data acquisition in operations research applications and demonstrate how coupling computer vision with optimization and simulation can enhance decision-making in retail operations. The proposed framework provides a practical pathway for improving both operational efficiency and model-driven resource allocation in data-constrained environments.

Biography: Dr. Eugene Rex L. Jalao is a Professor of Analytics and Industrial Engineering in the University of the Philippines Diliman, Department of Industrial Engineering and Operations Research. He is also the Program Coordinator of the Artificial Intelligence Program of UPD. He specializes in Decision Support Systems, Business Analytics Solutions, Data Mining, Optimization and Systems Simulation. He obtained his Ph.D. in Industrial Engineering from Arizona State University (ASU) in May 2013. Additionally, he obtained his Masters of Science in Industrial Engineering degree as well as his Bachelor of Science in Industrial Engineering from the University of the Philippines Diliman in 2009 and 2007 respectively. His fifteen years of work and research experience are in the fields of business analytics both here in the Philippines and in the United States of America, specifically in the Banking, FMCG, Manufacturing, Real Estate, Healthcare, Telecommunications and Information Technology industries. He is also a certified SAP ERP Materials Management consultant, a Matlab computing associate, a Certified NVIDIA Deep Learning Instructor and an advocate of the R and Python Programming languages.

Prof. Rammohan Mallipeddi

Kyungpook National University, South Korea

Biography: Dr. Rammohan Mallipeddi, a Senior Member of IEEE, is a Full Professor in the Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University, Daegu, South Korea. He earned his master’s and Ph.D. degrees in computer control and automation from Nanyang Technological University, Singapore, in 2007 and 2010, respectively. A globally recognized researcher, he ranks among the top 2% of most-cited researchers worldwide, with over 9,000+ google scholar citations and an h-index of 40.

Dr. Mallipeddi's research interests span evolutionary computing, artificial intelligence, image processing, digital signal processing, robotics, and control engineering. He has published 65 SCI/SCIE papers (2020–2024), including 35 in the top 10%, and collaborated with researchers from 12 countries. He is also an Associate Editor for prestigious journals, including IEEE Transactions on Cybernetics: Systems, Swarm and Evolutionary Computation, Information Sciences, Engineering Applications of Artificial Intelligence, etc.

He has held significant leadership roles, such as General Chair of the International Conference on Smart and Intelligent Systems (2021), Technical Program Chair of MIGARS (2023), and Program Chair for the IEEE Symposium on Differential Evolution since 2018.

My google scholar Profile: https://scholar.google.com.sg/citations?user=bCJAc_8AAAAJ&hl=en

My Lab Website: https://ecis.knu.ac.kr/

Invited Speakers

Prof. Sunny Joseph Kalayathankal

Rajagiri School of Engineering & Technology, India

Speech Title: Fuzzy Modelling and Decision Making Applications in the Real World

Abstract: The thought process involved in the act of decision making is a complex array of streaming possibilities in which a person selects or discards information made available from diverse sources. In doing so one is led by a meaningful analysis of available information and optimal selection out of several apparently equi-efficient decisions. Since Zadeh (1965) published the fuzzy set theory as an extension of classic set theory, it has been widely used in many fields of application, such as pattern recognition, data analysis, system control, management etc. The unique characteristic of this theory, in contrast to classic mathematics, is its operation on various membership functions (MF) instead of the crisp real values of the variables. Molodtsov (1999) initiated the concept of soft set theory as a new mathematical tool for dealing with uncertainties. Pabitra Kumar Maji et al. (2001) introduced fuzzy soft set theory which also deals with uncertainties. Out of the several higher order fuzzy sets, intuitionistic fuzzy sets by Atanassov (1985) and Ordered intuitionistic fuzzy sets proposed by Kalayathanal et al. (2010) have been found to be highly useful to deal with vagueness. Intuitionistic fuzzy set is described by two functions: a membership function and a non - membership function. We develop and apply similarity measures between ordered intuitionistic fuzzy sets to multiple attribute decision making (MADM) under fuzzy environment.

Biography: Prof. Dr. Sunny Joseph Kalayathankal received the MSc. degree from Kerala University, Kerala, India in 1986, B.Ed from Calicut University, Kerala in 1987, MPhil from Kerala University in 1993 and Ph.D (Mathematics) degree in 2010 from Kerala University, MCA from Indira Gandhi National Open University, New Delhi, India in 2002, M.Tech IT from Karnataka State Open University in 2013 and Ph.D. in Computer Science under Bharathiar University, Coimbatore, India in 2018. He was the Head of the Department of Mathematics, K.E.College, Mannanam, Kottayam, Former Principal of Jyothi Engineering College Cheruthuruthy, Trissur , Former Director of Research in Jyothi Engineering College Affiliated to APJ Abdul Kalam Technological University, Kerala India. He is currently working as Professor of Computer Science & Engineering in Rajagiri School of Engineering & Technology, Kerala , India and has 38 years of teaching and 20 years of research experience. He has published more than 116 papers in the area of Fuzzy Modelling and Decision Making, Graph Theory and Applied Mathematics. He has produced 3 Ph.Ds in the area of Graph Theory and Fuzzy Modelling. He has served as 66 Keynote and Invited Speaker in various National and International Conferences. He is the Research guide of APJ Abdul Kalam Technological University, M.G.university Kottayam and Bharathiar University Coimbatore. He is the reviewer of Iranian Journal of Fuzzy System, International Journal of Fuzzy System and Journal of Mathematical Modeling and Computer Simulation.

Prof. Zong Woo Geem

Gachon University, South Korea

Speech Title: Music-Inspired Harmony Search Algorithm and its Applications in Southeast Asia

Abstract: The Harmony Search (HS) algorithm is a music-inspired optimization technique and has been so far applied to various optimization problems in engineering, science, sociology, management, arts, etc. This talk briefly introduces a basic theory of the HS algorithm and its various applications performed in Southeast Asian countries (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Viet Nam, etc). The theory part of HS includes the basic structure of the HS algorithm, and its unique human-experience-based derivative. And, the application part includes optimal scheduling in residential buildings in Brunei; optimal feature selection of EEG signals in Myanmar; discrete sizing optimization of truss structure in Indonesia; optimal power flow solution in Thailand; flood-susceptibility prediction in Vietnam; real-time implementation of clustering protocol for energy-efficient wireless sensor networks in Singapore; and university course timetabling in Malaysia. While the HS algorithm is extensively studied in some countries, it is hardly researched in others, creating a striking contrast. It is hoped that research activity will become more active in the latter countries in the future.

Biography: Professor Zong Woo Geem is a prominent researcher and educator at Gachon University, widely recognized for his pioneering contributions to metaheuristic optimization. He is best known as the creator of the Harmony Search (HS) algorithm, a nature-inspired optimization method modeled after the improvisational process of musicians seeking the best harmony. Since its introduction, HS has become one of the most influential global optimization techniques, applied across engineering, energy systems, data science, smart cities, and more. Throughout his career, Professor Geem has held research and visiting scholar positions at leading institutions, including Virginia Tech, University of Maryland, and Johns Hopkins University, expanding the global reach of his research. His publication record is extensive, with numerous SCI-indexed papers each year, and he has been consistently recognized as one of the world’s Top 2% Scientists.
Website of Harmony Search Algorithm: https://sites.google.com/view/harmonysearch

Prof. Hiroyuki Sato

The University of Electro-Communications, Japan

Speech Title: Evolutionary Multi-objective Optimization in the Wild for Sustainable Industrial Systems

Abstract: This talk introduces real-world applications of evolutionary multi-objective optimization. In recent years, multi-objective optimization has attracted increasing attention as a framework for supporting practical decision making while considering multiple evaluation criteria simultaneously. In this talk, through several practical case studies, I demonstrate how evolutionary computation can contribute to decision making in industrial systems under real-world conditions, or "in the wild." Specifically, in e-commerce logistics, I present an inventory allocation framework that optimizes product placement across logistics centers distributed throughout Japan while considering conflicting objectives such as transportation cost and inventory volume. In manufacturing, I discuss a production scheduling method for cardboard manufacturing processes, where production sequences are optimized by considering manufacturing cost and postponed production. In building systems, I introduce parameter optimization for air-conditioning, lighting, and heat-source equipment, aiming to balance operational cost and human comfort. Finally, I summarize the role that multi-objective evolutionary computation can play in designing sustainable industrial systems and discuss challenges for future real-world applications.

Biography: Hiroyuki Sato received the M.E. and Ph.D. degrees from Shinshu University, Japan, in 2005 and 2009, respectively. He joined the University of Electro-Communications (UEC) in 2009 and is currently a Professor in the Department of Informatics. He is also affiliated with the Artificial Intelligence eXploration (AIX) Research Center at UEC. His research focuses on evolutionary computation, particularly evolutionary multi- and many-objective optimization, constrained optimization, and their applications to real-world problems. His work spans both fundamental algorithmic studies and practical projects, including collaborations with industry in areas such as production planning, design optimization, facility control, and intelligent systems integration. Dr. Sato has received several best paper awards, including those from the Genetic and Evolutionary Computation Conference (GECCO) in 2011, 2014, and 2022, and the IEEE Congress on Evolutionary Computation (CEC) in 2024, as well as multiple awards from the Transactions of the Japanese Society for Evolutionary Computation in 2012, 2015, 2020, and 2022. He is a member of IEEE, ACM SIGEVO, and the Japanese Society for Evolutionary Computation.

Assoc. Prof. Dmitri E. Kvasov

University of Calabria, Italy

Speech Title: Advanced global optimization techniques in machine learning

Abstract: In many simulation-based applications that employ machine learning techniques, the objective function can be multiextremal and non-differentiable, which precludes the use of derivative-based descent methods. Moreover, the function is often provided as a black box, making each evaluation computationally expensive. Derivative-free methods are therefore particularly well suited for addressing these challenging global optimization problems and can be either deterministic or stochastic in nature. A numerical comparison of these two classes of methods is of considerable interest for several reasons and has notable practical importance. In this presentation, methods from both groups are examined, and their applications in the field of machine learning are briefly surveyed.

Biography: Associate Professor in Numerical Analysis, DIMES, University of Calabria, Rende (CS), Italy. Italian National Scientific Habilitation as Full Professor in Numerical Analysis (2018–2027) and in Operations Research (2021–2030). Education: Ph.D. in Operations Research (05/2006), Department of Statistics, University of Rome "La Sapienza", Italy. Candidate (Ph.D.) of Physico-Mathematical Sciences (12/2016), "Lobachevsky" University of Nizhny Novgorod, Russia. Graduated, with honours, in Information Systems (06/2001), Faculty of Computational Mathematics and Cybernetics, "Lobachevsky" University of Nizhny Novgorod, Russia. Graduated, with honours, in Computer Systems Engineering (04/2001), Engineering Faculty, University of Calabria, Italy. Research interests: Numerical analysis; Continuous global optimization and applications; High-performance and Infinity computing. List of papers includes more than 130 items (among them: 2 research books).
Research Interests: Continuous global optimization and applications; high-performance and infinity computing
Webpage: http://people.dimes.unical.it/kvadim

Assoc. Prof. Ramesh Kumar Ayyasamy

Universiti Tunku Abdul Rahman (UTAR), Malaysia

Biography: Ramesh Kumar Ayyasamy (Senior Member, IEEE) earned his Ph.D. in Information Technology from Monash University, Australia, in 2013. He has over 22 years of teaching and research experience in Computer Science and Information Systems. He has held various academic and research roles at multiple institutions throughout his career. He is an Associate Professor in the Faculty of Information and Communication Technology at Universiti Tunku Abdul Rahman (UTAR), Malaysia. Dr. Ramesh's research expertise lies in AI-driven text analytics, focusing on sentiment analysis, deep learning for healthcare imaging, and semantic image segmentation. His work bridges theoretical foundations and practical applications, contributing to natural language processing, computer vision, smart city development, and health informatics. In addition to his research activities, he plays an active role in the academic community as a reviewer for leading journals and conferences. He serves on the editorial boards of several scholarly publications.

Dr. Shahin Jalili

Imperial College London, UK

Speech Title: A Hyper-Intelligent Algorithmic Framework for Large-Scale Optimisation

Abstract: The sensitivity of their performance to problem type and the associated computational complexities highlights the need for caution when applying standard metaheuristics to complex problems. Standard metaheuristic algorithms are prone to statistically unstable convergence behaviour or premature convergence. This talk will focus on introducing a novel algorithmic framework, termed hyper-intelligent (HI). The framework is based on the hypothesis that the simultaneous, yet intelligent, application of multiple population-based metaheuristics yields better results than applying them individually. In HI algorithms, each individual has access to a high-level heuristic (HLH) subspace that provides an online, feedback-based mechanism for learning and adapting its behaviour throughout the search process. By interacting with the low-level heuristic (LLH) space, individuals record experiences in their HLH subspaces, which then guide the selection of the most appropriate metaheuristics at different stages of optimisation.

Biography: Shahin is an independent Departmental Fellow in the Department of Civil and Environmental Engineering, specialising in decision-making and optimisation for decarbonising diverse sectors, including buildings, transportation, offshore wind energy, and oil and gas. He has developed a series of intelligent algorithms to reduce the economic, environmental, safety, and societal impacts of built environment infrastructure. With several years of research experience at Imperial, the University of Exeter, the University of Aberdeen, and the National Decommissioning Centre, he has collaborated closely with international energy companies and a diverse range of stakeholders to create innovative and practical solutions that support the transition to net zero. Shahin is also a Chartered Engineer with the Institution of Mechanical Engineers (IMechE) and is affiliated with the Grantham Institute and Imperial-X.