Zbigniew Michalewicz

Last updated

Zbigniew Michalewicz
Education Master of Science Applied mathematics
PhD Computer science
Alma mater Polish Academy of Sciences
Occupation(s)Chief Scientific Officer at Complexica
author
professor [1]
OrganizationComplexica
Known for Optimization

Zbigniew Michalewicz is an entrepreneur, author and professor in the fields of mathematical optimisation and new technologies. [2] He is the author of over 250 articles and 25 books which have been widely cited. [3] He is the co-founder of NuTech Solutions, [4] SolveIT Software, and Complexica where he currently serves as the Chief Scientific Officer. [5] [6]

Contents

Early life and education

Michalewicz attended Warsaw University of Technology where he earned a Master of Science degree in Applied Mathematics in 1974. In 1975, Michalewicz joined the faculty of the Institute of Computer Science at the Polish Academy of Sciences as a researcher. While there, he obtained a PhD in Computer Science in 1981; and a Doctor of Science (Habilitation) degree in Computer Science in 1997. [7]

Professional career

In 1982, Michalewicz left Poland and moved to New Zealand with his wife Ewa and son Matthew Michalewicz. He took a position with Victoria University of Wellington, leaving behind all of his possessions in Poland with the exception of a few personal items. In an interview, he stated that he left Poland for many reasons including the difficult economy at that time and the lack of access to scientific publications to conduct research. [7] He emigrated to Charlotte, North Carolina in July 1989, just prior to Hurricane Hugo. He began working for University of North Carolina at Charlotte where he received an offer of employment one year prior while he was still at the Victoria University. [7] He taught at UNC Charlotte in the Department of Computer Science through 2004.

In 2004, Michalewicz accepted a position with the University of Adelaide as well as the Polish-Japanese School of Information Technology. Michalewicz is currently a professor at the University of Adelaide, [1] the Polish-Japanese School of Information Technology, and the Institute of Computer Science of the Polish Academy of Sciences. [2]

Michalewicz also consulted on data mining and optimisation projects for companies and governmental agencies in the United States, Australia, and Poland. He served as the Chairman of the Technical Committee on Evolutionary Computation and also as the Executive Vice-President of the Institute of Electrical and Electronics Engineers Neural Network Council. He was the general chair of the First IEEE Congress on Evolutionary Computation held in Orlando in June 1994. [8]

NuTech Solutions

Michalewicz co-founded NuTech Solutions, Inc. in 1999 with his son Matthew. NuTech offered technology solutions to predict business changes and helped companies prepare for these changes. In September 2003, Matthew left the company after resigning from the board of directors. [9] Towards the end of 2004, he moved from Charlotte to Adelaide, Australia.

In 2005, Michalewicz sold his shares of NuTech back to the company to make way for new investments into the company. [10] NuTech was acquired by Netezza Corporation in 2008 and in 2010, IBM Corporation acquired Netezza and became the licensor of the technology licensed by NuTech. [11]

SolveIT Software

Michalewicz is the co-founder of SolveIT Software which was founded in 2005, a few months after his arrival in Australia. The other co-founders are Matthew Michalewicz, Martin Schmidt, and Constantin Chiriac. All were the co-authors of the book Adaptive Business Intelligence. [12]

The company develops advanced planning and scheduling business optimisation software, which helps manage complex operations using artificial intelligence. Most of the products were initially developed around the key South Australian industries of wine and grain handling, and today SolveIt has a specialist mining division due to early adoption of the companies solutions within the mining market. [13] The software helps companies accurately predict and plan their production, supply chain, shipping and currency hedging. [14] [15]

Complexica

Complexica, artificial intelligence software provider, was co-founded by Zbigniew and Matthew Michalewicz. [16]

Publications and lectures

Select articles

Michalewicz has been published in numerous journals including the IEEE Transactions on Evolutionary Computation – TEC. [17]

Bibliography

YearTitlePublisherISBN
2012Variants of Evolutionary Algorithms for Real-World Applications Springer Nature ISBN   3642234232 [18]
2010How to Solve It: Modern Heuristics ISBN   3642061346
Parameter Setting in Evolutionary Algorithms (Studies in Computational Intelligence) ISBN   3642088929 [19]
2008Puzzle-Based Learning: Introduction to Critical Thinking, Mathematics, and Problem Solving ISBN   1876462639
Simulated Evolution and Learning: 7th International Conference, SEAL 2008, Melbourne, Australia, 7–10 December 2008, Proceedings ISBN   3540896937
Design By Evolution: Advances In Evolutionary Design (Natural Computing Series) ISBN   3540741097 [20]
Advances in Metaheuristics for Hard Optimization ISBN   3540729593 [21]
2007Winning CredibilityHybrid Publishers ISBN   1876462523
2006Adaptive Business IntelligenceSpringer Nature ISBN   3540329285
2004How to Solve It: Modern Heuristics ISBN   3540224947
2002Theoretical Surface Science: A Microscopic Perspective ISBN   354043903X
2001Evolutionary Algorithms in Engineering Applications ISBN   3642082823
2000Advanced Algorithms and Operators IOP Publishing ISBN   0750306653
1993Genetic Algorithms + Data Structures = Evolution ProgramsSpringer Nature ISBN   3540606769
1990Statistical and Scientific Database Management: Fifth International Conference ISBN   3540523421

Awards and recognition

Michalewicz was awarded the title of Professor in 2002 by then Polish President Aleksander Kwasniewski. [2] He was appointed as a Business Ambassador for South Australia in 2006 by then Premier Mike Rann. [22] He was also named as a finalist for South Australia's Science Excellence Awards for the commercialisation success of Adapative Business Intelligence in 2008 [23] as well as a 2010 DSI Instructional Innovation Award Competition Finalist. [24]

He was the recipient of the prestigious Pearcey Award which recognises young entrepreneurs in the ICT space that have made significant contributions and taken entrepreneurial risks. He was given the award in 2011 for his founding of SolveIT Software. [25]

Personal life

Michalewicz is married to Ewa Michalewicz, an artist [26] who also did the cover work for Michalewicz's book How To Solve It. [27] Michalewicz has a son, Matthew Michalewicz who is the co-author of some of Zbigniew Michalewicz's books, including Winning Credibility, Puzzle-Based Learning and Adaptive Business Intelligence. Matthew also co-founded SolveIT Software with his father Zbigniew. [28]

Related Research Articles

<span class="mw-page-title-main">Genetic algorithm</span> Competitive algorithm for searching a problem space

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

<span class="mw-page-title-main">Evolutionary algorithm</span> Subset of evolutionary computation

In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.

In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

<span class="mw-page-title-main">Particle swarm optimization</span> Iterative simulation method

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.

A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. Fitness functions are used in evolutionary algorithms (EA), such as genetic programming and genetic algorithms to guide simulations towards optimal design solutions.

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.

The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.

Search-based software engineering (SBSE) applies metaheuristic search techniques such as genetic algorithms, simulated annealing and tabu search to software engineering problems. Many activities in software engineering can be stated as optimization problems. Optimization techniques of operations research such as linear programming or dynamic programming are often impractical for large scale software engineering problems because of their computational complexity or their assumptions on the problem structure. Researchers and practitioners use metaheuristic search techniques, which impose little assumptions on the problem structure, to find near-optimal or "good-enough" solutions.

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.

Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems.

<span class="mw-page-title-main">Roberto Battiti</span>

Roberto Battiti is an Italian computer scientist, Professor of computer science at the University of Trento, director of the LIONlab, and deputy director of the DISI Department and delegate for technology transfer.

LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling and reactive business intelligence approach. A non-profit version is also available as LIONoso.

SolveIT Software Pty Ltd is a provider of advanced planning and scheduling enterprise software for supply and demand optimisation and predictive modelling. Based in Adelaide, South Australia, 70% of its turnover is generated from software deployed in the mining and bulk material handling sectors.

<span class="mw-page-title-main">Matthew Michalewicz</span> Australian entrepreneur

Matthew Michalewicz is an Australian entrepreneur and author with experience in the fields of technology, commercialization and supply chain management. He is the co-author of a number of books and publications, some of which have been adapted into courses on problem solving in colleges and universities. He is the co-founder of NuTech Solutions, SolveIT Software, and Complexica, companies that he started with his father Zbigniew Michalewicz. In 2012, SolveIT Software Pty Ltd, was acquired by Schneider Electric.

The constructive cooperative coevolutionary algorithm (also called C3) is a global optimisation algorithm in artificial intelligence based on the multi-start architecture of the greedy randomized adaptive search procedure (GRASP). It incorporates the existing cooperative coevolutionary algorithm (CC). The considered problem is decomposed into subproblems. These subproblems are optimised separately while exchanging information in order to solve the complete problem. An optimisation algorithm, usually but not necessarily an evolutionary algorithm, is embedded in C3 for optimising those subproblems. The nature of the embedded optimisation algorithm determines whether C3's behaviour is deterministic or stochastic.

<span class="mw-page-title-main">Glossary of artificial intelligence</span> List of definitions of terms and concepts commonly used in the study of artificial intelligence

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

<span class="mw-page-title-main">Emma Hart (computer scientist)</span> English computer scientist

Professor Emma Hart, FRSE is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evolutionary Computation, and D. Coordinator of the Future & Emerging Technologies (FET) Proactive Initiative, Fundamentals of Collective Adaptive Systems.

<span class="mw-page-title-main">Babak Hodjat</span> Co-founder and CEO of Sentient Technologies

Babak Hodjat was the co-founder and CEO of Sentient Technologies and now holds the position of Vice President of Evolutionary AI at Cognizant. He is a specialist in the field of artificial intelligence and machine learning.

<span class="mw-page-title-main">Chan-Jin Chung</span> Computer science professor (born 1959)

Chan-Jin Chung (정찬진) or popularly known as "CJ" Chung is a full professor of Computer Science at Lawrence Technological University(LTU) in Michigan, USA. He founded an international autonomous robotics competition called Robofest in the 1999–2000 academic year as well as numerous educational programs for youth by integrating STEM, arts, autonomous robotics, and computer science. He also served as the founding USA National Organizer of World Robot Olympiad (WRO) in 2014 and 2015. He also started the WISER conference in 2014. He is working on developing a computer science curriculum for connected and autonomous vehicles (CAV) with a support from National Science Foundation . His research areas include evolutionary computation, cultural algorithms, intelligent systems & autonomous mobile robotics, software engineering, machine learning & deep learning, computer science education, and educational robotics.

References

  1. 1 2 Adelaide University. "University Staff Directory" . Retrieved 24 July 2012.
  2. 1 2 3 Adaptive Business Intelligence. "Author Info". Archived from the original on 7 August 2013. Retrieved 24 July 2012.{{cite web}}: |last= has generic name (help)
  3. Microsoft Academic Search. "Academic Authors – Zbigniew Michalewicz" . Retrieved 24 July 2012.
  4. Inside View. "NuTech Solutions, Inc" . Retrieved 26 July 2012.
  5. Logistics Weekly. "SolveIT Software Wins National IAward" . Retrieved 26 July 2012.
  6. "People".
  7. 1 2 3 WRAL Techwire (18 August 2008). "Life Is an Adventure for NuTech's Zbigniew Michalewicz" . Retrieved 24 July 2012.
  8. JICAN. "Zbigniew Michalewicz" . Retrieved 24 July 2012.
  9. WRAL Tech Wire (18 August 2008). "Founding CEO Matt Michalewicz Is Out at NuTech Solutions" . Retrieved 26 July 2012.
  10. Charlotte Business Journal. "Deal ends founders' ties to NuTech" . Retrieved 26 July 2012.{{cite web}}: |last= has generic name (help)
  11. NuTech Solutions. "Company Overview" . Retrieved 26 July 2012.
  12. Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac (10 November 2006). Adaptive Business Intelligence®. Springer Publishing. ISBN   3540329285.{{cite book}}: CS1 maint: multiple names: authors list (link)
  13. "Mine optimisation and mining solutions". SolveIT Software. Retrieved 26 July 2012.
  14. Cameron England (9 January 2012). "SolveIT Software is wining and mining". The Advertiser . Retrieved 26 July 2012.
  15. "About Us". SolveIT Software. Retrieved 26 July 2012.
  16. "Company".
  17. Microsoft Academic Search. "Journals Related To Zbigniew Michalewicz" . Retrieved 26 July 2012.
  18. Good Reads. "Variants of Evolutionary Algorithms for Real-World Applications" . Retrieved 24 July 2012.
  19. Good Reads. "Parameter Setting in Evolutionary Algorithms" . Retrieved 24 July 2012.
  20. Amazon.com (12 September 2008). Design By Evolution: Advances In Evolutionary Design (Natural Computing Series). Springer. ISBN   978-3540741091.
  21. Springer Nature. Advances in Metaheuristics for Hard Optimization . Retrieved 24 July 2012.
  22. Adelaidean. "Business ambassador" . Retrieved 24 July 2012.
  23. SolveIT Software. "SolveIT Founder Named Finalist for Science Excellence Awards" . Retrieved 24 July 2012.
  24. Decision Sciences Institute. "2010 DSI Instructional Innovation Award Competition Finalist" . Retrieved 26 July 2012.
  25. Adelaide University. "School staff win SA Pearcey Award Again!" . Retrieved 26 July 2012.
  26. Ewa Michalewicz. "About Ewa" . Retrieved 26 July 2012.
  27. Zbigniew Michalewicz; David B. Fogel (21 September 2004). How To Solve It. Springer. ISBN   9783540224945 . Retrieved 26 July 2012.
  28. Amazon.com (May 2007). Winning Credibility. Hybrid Publishers. ISBN   978-1876462529.