# Zbigniew MichalewiczUniversity of Adelaide · School of Computer Science

Zbigniew Michalewicz

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350

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Introduction

## Publications

Publications (350)

<this is the preliminary version of https://www.springer.com/us/book/9783030016401>
Over the past 30 years, many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing ei...

Despite its known shortcomings, penalty function approaches are among the most commonly used constraint handling methods in the field of evolutionary computation. In this paper, we argue that some of the techniques used to alleviate these shortfalls (namely scaling and normalisation) cannot avoid undesired search biases. Instead, we introduce the n...

Real-world optimization problems have been studied in the past, but the work resulted in approaches tailored to individual problems that could not be easily generalized. The reason for this limitation was the lack of appropriate models for the systematic study of salient aspects of real-world problems. The aim of this paper is to study one of such...

AVAILABLE ONLINE FOR FREE AT:
http://www.mitpressjournals.org/doi/pdf/10.1162/EVCO_r_00180
This paper reviews recent studies on Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

In this paper we investigate movement patterns of a particle in the particle swarm optimization (PSO) algorithm. We characterize movement patterns of the particle by two factors: the correlation between it’s consecutive positions and it’s range of movement. We introduce the base frequency of movement as a measure for the correlation between positio...

Real-world problems are usually composed of two or more (potentially NP-Hard) problems that are interdependent on each other. Such problems have been recently identified as "multi-hard problems" and various strategies for solving them have been proposed. One of the most successful of the strategies is based on a decomposition approach, where each o...

In this paper we present a challenging problem that many decision makers in coal mining industry face. The coal processing and blending problem (CPBP) builds upon the traditional blending problem known in operations research (OR) by including decision variables around coal processing, novel constraints as well as arbitrary user-defined profit funct...

The objective of this paper is to show that the ability of nature-inspired optimization routines to construct complex models does not necessarily imply any improvement in performance. In fact, the reverse may be the case. We demonstrate that under the dynamic conditions found in most financial markets, complex prediction models that seem, ex-ante,...

Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing either optimal or near optimal solution, was of major significance. Indeed, this was a v...

Designing automatic drivers for car racing is an active field of research in the area of robotics and artificial intelligence. A controller called Ahura (A HeUristic-based RAcer) for The Open Racing Car Simulator (TORCS) is proposed in this paper. Ahura includes five modules, namely steer controller, speed controller, opponent manager, dynamic adju...

In this letter we study the first and second-order stabilities of a stochastic recurrence relation that represents a class of particle swarm optimization algorithms. We assume that the personal and global best vectors in that relation are random variables (with arbitrary means and variances) that are updated during the run so that our calculations...

Appropriate scheduling of the manufacturing process plays an important role in improving energy efficiency besides the adoption of new production equipment. A trend has emerged in the transportation sector to employ alternative fuel vehicles, with the aim of reducing the emission of greenhouse gases and toxic air pollutants. Scheduling the new type...

The productivity of real-world systems is often limited by so-called bottlenecks. Hence, usually companies are not only interested in finding the best ways to schedule their current resources so that their benefits are maximized (optimization), but, in order to increase the productivity, they also conduct some analysis to find bottlenecks in their...

In constrained optimization problems set in continuous spaces, a
feasible search space may consist of many disjoint regions and the global optimal
solution might be within any of them. Thus, locating these feasible regions (as
many as possible, ideally all of them) is of a great importance. In this chapter, we
introduce niching techniques that...

Abstract-We introduce an experimentation procedure for evaluating and comparing optimization algorithms based on the Traveling Salesman Problem (TSP). We argue that end-of-run results alone do not give sufficient information about an algorithm's performance, so our approach analyzes the algorithm's progress over time. Comparisons of performance cur...

Real-world optimization problems often consist of several NP-hard optimization problems that interact with each other. The goal of this manual is to describe a benchmark suite that promotes a research of the interaction between problems and their mutual influence. We establish a comprehensive benchmark suite for the traveling thief problem (TTP) wh...

Real-world optimization problems often consist of several NP-hard optimization problems that interact with each other. The goal of this paper is to provide a benchmark suite that promotes a research of the interaction between problems and their mutual influence. We establish a comprehensive bench-mark suite for the traveling thief problem (TTP) whi...

In a particle swarm optimization algorithm (PSO) it is essential to guarantee convergence of particles to a point in the search space (this property is called stability of particles). It is also important that the PSO algorithm converges to a local optimum (this is called the local convergence property). Further, it is usually expected that the per...

Many real-world problems are composed of two or more problems that are interdependent on each other. The interaction of such problems usually is quite complex and solving each problem separately cannot guarantee the optimal solution for the overall multi-component problem. In this paper we experiment with one particular 2-component problem, namely...

In many real-world constrained optimization problems (COPs) it is highly probable that some constraints are active at optimum points, i.e. some optimum points are boundary points between feasible and infeasible parts of the search space. A method is proposed which narrows the feasible area of a COP to its boundary. In the proposed method the thickn...

In this paper, we present evolutionary racer (EVOR) that is a simulated car dynamically controlled by an online evolutionary algorithm (EA). The key distinction between EVOR and earlier car racing methods is that it considers car racing as a dynamic optimization problem which is addressed by an evolutionary algorithm. Our approach calculates a car...

Purpose
– This is the first part of a two-part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) to investigate an Evolutionary Algorithm (EA) and memory-based approach referred to as McBAR – the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Some of the methods are u...

For constrained optimization problems set in a continuous space, feasible regions might be disjointed and the optimal solution might be in any of these regions. Thus, locating these feasible regions (ideally all of them) as well as identifying the most promising region (in terms of objective value) at the end of the optimization process would be of...

Purpose: This is the first part of a two-part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) to investigate an Evolutionary Algorithm (EA) and memory-based approach referred to as McBAR - the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Some of the methods are us...

The amount and type of teacher–student interaction in a Puzzle-based Learning course can vary widely based on the number of students in the course. In our experience, we have taught this course to as many as 300 and as few as 10. Irrespective of the size of the class, it is important that students are active rather than passive. The goal of the tea...

In this chapter we look at one of the biggest stumbling blocks for students and teachers alike: working out what the problem actually is so that we can solve the right problem. When approaching puzzles, some people feel overwhelmed because they can’t even start on a path to a solution. We show you in this chapter that with some preparation and prac...

The word geometry comes from the ancient Greek geo, meaning earth, and metron, meaning measurement. So, geometry originally meant measuring the earth. Today geometry has expanded to include the study of two- and three-dimensional shapes as well as how multiple three-dimensional shapes are connected and how multiple two-dimensional shapes will tesse...

One of the challenges in implementing a Puzzle-based Learning approach is taking a love of puzzles, or a desire to make students think in a more open-ended fashion, and making it work in a classroom environment. Many courses reward students for sitting quietly and, when prompted, answering a set of well-defined questions with rehearsed answers buil...

Much of being a good problem-solver is utilizing clever strategies to make the solution to the problem more accessible. One of the most useful of these strategies is to simplify the problem. There are a number of different simplifications that will lead to progress towards the solution. One of these is to simply restate or rephrase the problem in t...

When we discuss with colleagues our motivation and experience in teaching Puzzle-based Learning, a question that quickly follows from those interested in exploring this paradigm further is: How can I do this in my university? Given our engagement with teaching Puzzle-based Learning in a range of settings and to a range of audiences, in this chapter...

Many real-world problems are so complex that it is impossible to conduct a full theoretical analysis. In such cases, we can turn to simulation – we make experiments and carefully record the results. We have already suggested simulation when we discussed Problem 7.5, where different tennis players might have different probabilities of winning their...

Our ability to recognize patterns is very useful in solving a variety of problems. Once we identify the pattern, it might be easier to suggest a solution – whether this might be to predict the next (or missing) symbol, number, action, or event (in the same way that fraud detection systems try to discover patterns in historical data and then use the...

Probability theory is the branch of mathematics that deals with estimating or calculating the degrees of likelihood. If it is impossible that a particular event would happen, it is given a probability of zero. If it is certain that a particular event would happen, it is given a probability of one. The probabilities of other events (expressed as fra...

One of the most powerful (and popular) problem-solving techniques applicable to many problems is a technique that is based upon the enumeration of all possible solutions and the systematic elimination of solutions which are “wrong.” By repeating this process of elimination, we zoom in into the (usually relatively small) subset of possible solutions...

This chapter contains a set of problems that do not require any high-level mathematics or formal training in logic. They do not require any knowledge of vocabulary or culture. There are no “tricks.” The problems just require a focused mind that is able to ask the appropriate “What if” questions and then follow the line of reasoning to the only resu...

A gedanken (from the German) is a thought experiment, a hypothesis that is evaluated in the mind. There can be many reasons to perform a gedanken. One is that the actual experiment is too difficult or even impossible to perform. Many great discoveries are made with or start from a gedanken. Albert Einstein wondered how a light beam would look if he...

Here is a collection of challenging problems that require multiple problem-solving strategies and a solid foundation in understanding and framing the problem. These can be used as grand challenges that advanced students work on outside of the classroom or as a “special bonus” if the class is doing well.

Starting from the end of the problem and working backwards to the original condition is a problem-solving technique that should be part of any good problem-solver’s arsenal. This is a problem-solving technique that is well known and is used in many disciplines. It is also known as retrograde analysis, backward chaining, and backward induction. As H...

Consider the following puzzles. Some of the solutions to these are discussed in detail in further chapters. For now, just ponder the puzzles themselves.
Given two eggs, for a 100-story building, what would be an optimal way to determine the highest floor, above which an egg would break if dropped?
Suppose you buy a shirt at a discount. Which is mor...

During the last few years, most production-based businesses have been under enormous pressure to improve their top-line growth and bottom-line savings. As a result, many companies are turning to systems and technologies that can help optimise their supply chain activities. In this paper, we discuss a real-world application of scheduling in the mini...

In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immig...

The particle swarm optimization algorithm includes three vectors associated with each particle: inertia, personal, and social influence vectors. The personal and social influence vectors are typically multiplied by random diagonal matrices (often referred to as random vectors) resulting in changes in their lengths and directions. This multiplicatio...

This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that fol...

This book provides insights drawn from the authors extensive experience in teaching Puzzle-based Learning. Practical advice is provided for teachers and lecturers evaluating a range of different formats for varying class sizes. Features: suggests numerous entertaining puzzles designed to motivate students to think about framing and solving unstruct...

Cyber-enabled devices are becoming more and more complex with integration of new capabilities and functionalities, both in software and hardware, making it very difficult for users to realize that they are under cyber attack or the cause of data breach, etc. It is also well-known fact that vulnerabilities at one component can affect other component...

This article describes a decision-support system that was developed in 2011 and is currently in production use. The purpose of the system is to assist planners in constructing delivery schedules of water tanks to often remote areas in Australia. A delivery schedule consists of a number of delivery trips by trucks. An optimal delivery schedule minim...

This chapter deals with the problem of balancing and optimising the multi-echelon supply chain network of an Australian ASX Top 50 company which specialises in the area of manufacturing agricultural chemicals. It takes into account sourcing of raw material, the processing of material, and the distribution of the final product. The difficulty of mee...

Two approaches for solving numerical continuous domain constrained optimization problems are proposed and experimented with. The first approach is based on particle swarm optimization algorithm with a new mutation operator in its velocity updating rule. Also, a gradient mutation is proposed and incorporated into the algorithm. This algorithm uses ε...

There are some questions concerning the applicability of meta-heuristic methods for real-world problems; further, some researchers claim there is a growing gap between research and practice in this area. The reason is that the complexity of real-world problems is growing very fast (e.g. due to globalisation), while researchers experiment with bench...

A novel hybrid algorithm is proposed to solve the Australian wheat blending problem. The major part of the problem can be modeled with a linear programming model but the unique constraints make many existing algorithms fail. The algorithm starts with a heuristic that follows pre-defined rules to reduce the search space. Then the linear-relaxed prob...

We describe a planning system for multi-mine scheduling that works by iteratively interrogating a single-mine planner for each individual mine-site. At the heart of the system is a multi-objective evolutionary algorithm that runs in every iteration to derive a set of requests to present to the single-mine planners. These requests are optimised to b...

Purpose: The purpose of this paper and its companion (Part II: multi-silo supply chains) is to investigate methods to tackle complexities, constraints (including time-varying constraints) and other challenges. In tis part, the paper aims to devote attention to single silo and two-silo supply chains. It also aims to discuss three models. The first m...

Purpose: The purpose of this paper and its companion (Part I: single and two-component supply chains) is to investigate methods to tackle complexities, constraints (including time-varying constraints) and other challenges. In this part, attention is devoted to multi-silo supply chain and the relationships between the components. The first part of t...

During the past 35 years the evolutionary computation research community has been studying properties of evolutionary algorithms. Many claims have been made---these varied from a promise of developing an automatic programming methodology to solving virtually any optimization problem (as some evolutionary algorithms are problem independent). However...

This paper examines the interaction of decision model complexity and utility in a computational intelligence system for algorithmic trading. An empirical analysis is undertaken which makes use of recent developments in multiobjective evolutionary fuzzy systems (MOEFS) to produce and evaluate a Pareto set of rulebases that balance conflicting criter...

The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary
Algorithms unclear. Originally, it was coined to put a group of stochastic search algorithms that mimic natural evolution together. While some people would still see it as a specific term devoted to this group of algorit...

Practical constraints associated with real-world problems are a key differentiator with respect to more artificially formulated problems. They create challenging variations on what might otherwise be considered as straightforward optimization problems from an evolutionary computation perspective. Through solving various commercial and industrial pr...

At the Workshop on Evolutionary Algorithms, organized by the Institute for Mathematics and Its Applications, University of Minnesota, Minneapolis, Minnesota, October 21 – 25, 1996, one of the invited speakers, Dave Davis made an interesting claim. As the most recognised practitioner of Evolutionary Algorithms at that time he said that all theoretic...

A fast particle swarm optimization method for the multidimensional knapsack problem is presented. In this approach the potential solutions are represented by vectors of real values; the dimension of each vector corresponds to the number of constraints of the problem rather than the number of items. Each of these values measures the significance of...

— Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of object...

Puzzle-based learning (PBL) is an emerging model of teaching critical thinking and problem solving. Today's market place needs skilled graduates capable of solving real problems of innovation in a changing environment. While solving puzzles is innately fun, companies such as Google and Yahoo also use puzzles to assess the creative problem solving s...

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book "Variants of Evolutionary...

While computer science and engineering students are trained to recognise fa-miliar problems with known solutions, they may not be sufficiently prepared to address novel real-world problems. A successful computer science graduate does far more than just program and we must train our students to reach the required levels of analytical and computation...

While computer science and engineering students are trained to recognise familiar problems with known solutions, they may not be sucien tly prepared to address novel real-world problems. A successful computer science graduate does far more than just program and we must train our students to reach the required levels of analytical and computational...

Purpose – The purpose of this paper is to describe a real-world system developed for a large food distribution company which requires forecasting demand for thousands of products across multiple warehouses. The number of different time series that the system must model and predict is on the order of 10 5 . The study details the system's forecasting...

Fuzzy rules can be understood by people because of their specification in structured natural language. In a wide range of
decision support applications in business, the interpretability of rule based systems is a distinguishing feature, and advantage
over, possible alternate approaches that are perceived as “black boxes”, for example in facilitatin...

During the last few years most production-based businesses have been under enormous pressure to improve their top-line growth
and bottom-line savings. As a result, many companies are turning to systems and technologies that can help optimise their
supply chain activities and improving short- and long-term demand forecasting. Given the inherent comp...

Changes in environment are common in daily activities and can introduce new problems. To be adaptive to these changes, new solutions are to be found every time change occur. This two-part paper employs a technique called Centroid Based Adaptation (CBA) which utilize centroid of non-dominated solutions found through Multi-objective Optimization with...

Recent work combining population based heuristics and flexible models such as fuzzy rules, neural networks, and others, has led to novel and powerful approaches in many problem areas. This study tests an implementation of cellular evolution for fuzzy rule learning problems and compares the results with other related approaches. The paper also exami...

This paper proposes a metaheuristic selection tech- nique for controlling the progress of an evolutionary algorithm (and possibly other heuristic search techniques) to manipulate and make use of the relationship between runtime and solution quality. The paper examines the idea that very rapid increases in initial fitness may lead to premature conve...

This paper examines the advantages of simple mod- els over more complex ones for financial prediction. This premise is examined using a genetic fuzzy framework. The interpretability of fuzzy systems is oftentimes put forward as a unique advanta- geous feature, sometimes to justify effort associated with using fuzzy classifiers instead of alternativ...

Performance out of sample is a clear determinant of the usefulness of any prediction model regardless of the application. Fuzzy knowledge base systems are also useful due to interpretability; this factor is often cited as an advantage over black box systems which make model verification by expert users more difficult. Here we examine additional adv...

To improve evolutionary algorithm performance, this paper proposes a strategy to aid ascent and to help avoid premature convergence. Rapid increases in population fitness may result in premature convergence and sub optimal solution. A thresholding mechanism is proposed which discards child solutions only if their fitnesses are either too bad, in wh...

Any system (whether in the area of finance, manufacturing, administration, etc.) that operates in a dynamic environment needs to be adaptive to changes; it should also anticipate possible adverse events to remain competitive. In our previous research in this area we experimented with one particular approach: Mapping of Task ID for Centroid-Based Ad...