Seán McGarraghy

• PhD
• Lecturer at University College Dublin

In this paper, we consider the hybridisation of the team orienteering problem and the arc routing problem, the so-called team orienteering arc routing problem (TOARP). This problem has recently raised interest among researchers and practitioners as it can model new routing problems involving unmanned aerial vehicles or other types of electric vehic...

System dynamics and agent-based simulation modelling approaches have a potential as tools to evaluate the impact of policy related decision making in food value chains. The context is that a food value chain involves flows of multiple products, financial flows and decision making among the food value chain players. Each decision may be viewed from...

This paper reports on the problem of procedural and distributional fairness along the French wheat-to-bread food value chain and the specific hybrid approach of qualitative and quantitative modelling and simulation which was undertook to address the above issue from the perspective of socioeconomic sustainability of the supply chain system. The pap...

Food value chain systems are viewed as complex adaptive systems emerging out of market agents’ interactions and market price regulation; Managing such systems is explored through the stages of mapping their behaviour and then simulation of intervention scenarios. This work is part of an EU-funded project on understanding food value chains. We repor...

In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for ‘agile’ optimisation via parallel computing, and lear...

The Time Capacitated Arc Routing Problem (TCARP) extends the classical Capacitated Arc Routing Problem by considering time-based capacities instead of traditional loading capacities. In the TCARP, the costs associated with traversing and servicing arcs, as well as the vehicle’s capacity, are measured in time units. The increasing use of electric ve...

The metaphor of 'foraging as search' provides a rich source of inspiration for the design of optimisation algorithms. An extensive literature has resulted in computer science over the past twenty years based on this, with algorithmic families such as ant colony optimisation and honeybee optimisation amongst others, being successfully applied to a w...

Complexity arises in food supply chain systems from the points of view of managing fairness, sustainability and resilience, emerging out of agents interactions; this naturally suggests managing agent behaviour. The authors describe their approach to managing agents through the stages of mapping their behaviour and then simulation of intervention sc...

This paper discusses the Time Capacitated Arc Routing Problem (TCARP) and introduces a heuristic and a metaheuristic algorithm for solving large-size instances of it. The TCARP is a realistic extension of the Capacitated Arc Routing Problem in which edge-servicing and edge-traversing costs, as well as vehicle capacities, are all time-based—i.e., gi...

We then introduce algorithms inspired by the foraging activities of bioluminescent insects.

Most vertebrates have well-developed sensory capabilities and are generally capable of quite sophisticated cognition. Accordingly, they display a diverse array of foraging behaviours. In the next three chapters we describe a number of algorithms which have been inspired by the foraging activities of various fishes.

In this chapter we provide a brief introduction to some of the foraging behaviours of slime moulds and mycelial fungi. Growth in each organism is indeterminate, as their precise morphology is crucially determined by environmental influences, including resource distribution. We also describe a number of algorithms which have drawn inspiration from t...

We then introduce algorithms inspired by the foraging activities of social spiders.

We explore a series of algorithms which have been inspired by the foraging activities of a variety of invertebrate organisms. The best-known and most widely applied families of algorithms inspired by the foraging activities of invertebrate organisms are honeybee algorithms.

In this chapter we initially provide some background on plant behaviours, highlighting some of the key distinctions between plants and animals. Then we outline the comprehensive sensory capabilities of plants which help provide information to inform their foraging activities, and follow this with a description of plant aboveground and below-ground...

Most vertebrates have well-developed sensory capabilities and are generally capable of quite sophisticated cognition. Accordingly, they display a diverse array of foraging behaviours. In the next three chapters we describe a number of algorithms which have been inspired by the foraging activities of various mammals.

In Chapter 19 we clarify some of the differences between foraging and optimisation settings, so as to more clearly delineate limits to the foraging metaphor. Finally, we make some concluding comments and describe open research opportunities.

Provides an overview of some concepts from both individual and social learning, as well as covering important concepts from the social foraging literature.

We explore a series of algorithms which have been inspired by the foraging activities of a variety of invertebrate organisms. The best-known and most widely applied families of algorithms inspired by the foraging activities of invertebrate organisms are ant algorithms.

Provides an introduction to themes from sensory ecology, highlighting the primary sensory modalities found in nature. Past foraging experience and socially transmitted information from other animals are also important in a foraging context.

Discusses formal models from the foraging literature and also some concepts from the growing field of movement ecology. A key input into the foraging activity of animals is information captured by their senses.

In this chapter we introduce a number of studies which have applied evolutionary methodologies to evolve foraging strategies. Depending on the approach, evolutionary methodologies in conjunction with a simulated environment can be used to parameterise a predefined foraging strategy, or can be applied to uncover a complete foraging strategy from bas...

In this chapter we describe a number of bacterial foraging behaviours and demonstrate how these can be used to design optimisation algorithms. We also introduce a number of algorithms which are drawn from activities of even simpler biological agents, namely viruses, and illustrate how these agents can metaphorically be considered as foraging for ho...

Most vertebrates have well-developed sensory capabilities and are generally capable of quite sophisticated cognition. Accordingly, they display a diverse array of foraging behaviours. In the next three chapters we describe a number of algorithms which have been inspired by the foraging activities of various birds.

Having already introduced elements from various literatures which are relevant to the design of foraging-inspired search algorithms, Chapter 5 provides an introduction to the literature on foraging-inspired optimisation algorithms. In order to make this literature more accessible, a number of taxonomies of the various algorithms are presented. This...

In this chapter we describe five algorithms. First, we survey the optimal foraging algorithm which derives inspiration from optimal foraging theory. We then describe the group search optimiser algorithm and the predatory search algorithm which are inspired by the producer–scrounger foraging model and area-restricted search behaviour respectively.We...

We then introduce algorithms inspired by the foraging activities of the nematode worm C. elegans.

Recently, there has been a growing literature on biologically inspired algorithms, particularly genetic algorithms and genetic programming, applied to supply chain modelling and inventory control optimisation. Due to the rigidity of the genetic algorithms approach, it is difficult to change the underlying model logic and add richness to the supply...

The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be s...

Quite commonly, we are faced with the problem of taking a vector x = (x1, … , xn) of inputs and producing a vector y = (y1, … , ym) of outputs. For example, in a classification problem, the x1, … , xn may be characteristics of an item to be classified, and the corresponding output could be a single y, the class label for that item. Hence, the task...

In this chapter we introduce a family of algorithms whose workings draw inspiration from aspects of quantum mechanics in order to develop a series of hybrid quantum evolutionary algorithms. Initially, the chapter provides a short introduction to quantum systems and then describes the design of both hybrid binary-valued and hybrid real-valued quantu...

The previous chapter provided an overview of the main concepts behind the GA. Since the introduction and popularisation of the GA, a substantial body of research has been undertaken in order to extend the canonical model and to increase the utility of the GA for hard, real-world problems.While it is beyond the scope of any single book to cover all...

In this part of the book we discuss algorithms which are metaphorically inspired by a variety of social behaviours. The essence of these behaviours is that individuals can learn from both their own experience and from the experience of others. Hence a group can solve complex tasks which are beyond the capability of any of the individuals in the gro...

‘Owing to this struggle for life, variations, however slight and from whatever cause proceeding, if they be in any degree profitable to the individuals of a species, in their infinitely complex relations to other organic beings and to their physical conditions of life, will tend to the preservation of such individuals, and will generally be inherit...

Genetic programming (GP) was initially developed to allow the automatic creation of a computer program from a high-level statement of a problem’s requirements, by means of an evolutionary process. In GP, a computer program to solve a defined task is evolved from an initial population of random computer programs. An iterative evolutionary process is...

In this chapter, two significant evolutionary algorithms, evolution strategies and evolutionary programming, are presented. Evolution strategies (ES) was developed by Rechenberg and Schwefel [530, 531, 560, 561] in the 1960s and attracted a large following amongst researchers and practitioners, particularly in Europe. ES has been extensively used a...

Ants are not the only species of insect that use social communication to gather and process information from their environment in order to shape their behaviour. In this chapter we consider a range of mechanisms drawn from the behaviour of a variety of insects, including honeybees, glow worms and locusts, and see how these can stylistically inspire...

In Chap. 13, a series of NN models were described which can be used for supervised learning. In supervised learning the output for an associated input vector is already known and is used to guide the learning process. For example, in training a multilayer perceptron (MLP) the weights on arcs are adjusted in response to the difference which arises a...

In the final chapter in this part of the book, we introduce four emerging families of algorithms which have been inspired by various processes of social communication, in insects, bats and fish. Although these algorithms are not as fully developed or explored as ant or honey bee algorithms, they provide interesting examples of the diversity of natu...

In previous chapters we have introduced a wide array of natural computing algorithms and have illustrated how these fit into a broad taxonomy of algorithmic families. A recent addition to this taxonomy is a series of algorithms which are derived from studies of plant behaviours. In this chapter we initially outline a variety of interesting plant be...

To say that the knowledge uncovered by developmental biologists has been under-exploited in natural computing is perhaps an understatement. Curiously, despite the relative lack of research attention that has been paid to these important biological processes, one of the fathers of Computer Science, Alan Turing, recognised the power of developmental...

Following the introduction to a range of physical phenomena in the last chapter, this chapter describes a number of algorithms which are metaphorically inspired by these. As was the case with biologically inspired algorithms, the degree of faithfulness of each of these metaphors to the original natural process varies, and multiple algorithms could...

Bacteria are amongst the oldest and most populous forms of life on earth (a human typically has about 1014 bacteria in the gastrointestinal tract [499]). Despite possessing a relatively simple physical structure in comparison with mammals or insects, bacteria are capable of sophisticated interactions with their environment and with each other. Even...

At first glance the activities of insects do not appear to be an obvious source of inspiration for natural computing algorithms. However, on closer inspection it becomes apparent that many insects are capable of exceedingly complex behaviours. They can process a multitude of sensory inputs, modulate their behaviour according to these stimuli, and m...

A significant recent addition to the repertoire of grammar-based approaches to genetic programming (GP) is the use of tree-adjunct and tree-adjoining grammars (TAG). A TAG is a tree-generating grammar, and as such is a natural representation for GP and for computer programs. One of the primary benefits of a TAG is that the application of any rule w...

Physical systems and processes, just like biological ones, can inspire the design of computer algorithms. In this chapter we provide a short introduction to a range of physical phenomena, and in Chaps. 23 and 24 we outline a range of algorithms which draw inspiration from aspects of these phenomena. The nature of this material is necessarily somewh...

While the development of the genetic algorithm (GA) dates from the 1960s, this family of algorithms was popularised by Holland in the 1970s [281]. The GA has been applied in two primary areas of research: optimisation, in which GAs represent a population-based optimisation algorithm, and the study of adaptation in complex systems, wherein the evolu...

Differential evolution (DE) [520, 599, 600, 601] is a population-based search algorithm. It bears comparison with evolutionary algorithms such as the GA as it embeds implicit concepts of mutation, recombination and fitness-based selection. Like the GA, DE iteratively generates good solutions to a problem of interest by manipulating a population of...

The use of grammars in genetic programming (GP) has a long tradition, and there are many examples of different approaches in the literature representing linear, tree-based and more generally graph-based forms. McKay et al. [403] presented a survey of grammar-based GP in the 10th Anniversary issue of the journal Genetic Programming and Evolvable Mac...

The immune system of vertebrates is comprised of an intricate network of specialised tissues, organs, cells and chemical molecules. The capabilities of the natural immune system include the ability to recognise, destroy and remember an almost unlimited numbers of pathogens (foreign or nonself objects that can enter the body, such as viruses, bacter...

In this book we have described natural computing algorithms in terms of seven categories, namely, evolutionary computing (Part I), social computing (Part II), neurocomputing (Part III), immunocomputing (Part IV), developmental and grammatical computing (Part V), physical computing (Part VI), and other paradigms (Part VII). One cannot but be impress...

In this chapter we discuss neuroevolution — the application of an evolutionary process to uncover quality neural network models.While the chapter will focus on the evolution of MLPs, the concepts can be carried over to the evolution of other NN structures as well.

It has been observed that much of the diversity in the natural world can be traced to three features of developmental biology, namely, interactions between gene products, the temporal nature of gene expression, and shifts in the location of gene expression [32]. The first item highlights the significance of feedback loops in developmental processes...

An as yet relatively under-explored area of natural computing is the use of chemical phenomena as a source of inspiration for the design of computational algorithms. Of course, chemical processes play a significant role in many of the phenomena already described in this book, including (for example) evolutionary processes and the workings of the na...

Plants represent some 99% of the eukaryotic biomass of the planet and have been highly successful in colonising many habitants with differing resource potential. The success of plants in "earning a living" suggests that they have evolved robust resource capture mechanisms and reproductive strategies. In spite of the preponderance of plant life, sur...

This paper describes the Ring Spur Assignment Problem (RSAP), an interesting new problem arising in the design of Next Generation Networks. We describe the problem, position it in relation to problems previously addressed in the literature and give an IP model suitable for solving small problem instances. We outline a cutting plane algorithm for la...

Contributions to a supply chain's overall cost function (such as the bullwhip effect) are sensitive to the different players' ordering policies. This chapter addresses the problem of developing ordering policies which minimise the overall supply chain cost. Evolutionary Algorithms have been used to evolve such ordering policies. The authors of this...

Since its inception, πGE has used evolution to guide the order of how to construct derivation trees. It was hypothesised that this would allow evolution to adjust the order of expansion during the run and thus help with search. This research aims to identify if a specific order is reachable, how reachable it may be, and goes on to investigate what...

The ring spur assignment problem arises in the design of next-generation telecommunications networks and has applications in location-allocation problems. The aim is to identify a minimum cost set of interconnected ring spurs. We seek to connect all nodes of the network either on a set of bounded disjoint local rings or by a single spur edge connec...

This paper describes the ring spur assignment problem (RSAP), a new problem arising in the design of next generation networks. The RSAP complements the sonet ring assignment problem (SRAP). We describe the RSAP, positioning it in relation to problems previously addressed in the literature. We decompose the problem into two IP problems and describe...

Adaptive mutation operations have been proposed in Evolutionary Computation (EC) many times and in different varieties, but few have gained widespread use. In nature, mutation rates vary over time, however it has become common practice to use static, widely accepted, values for mutation, particularly in GP-like systems. In this study, an adaptive m...

We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position Independent Grammatical Evolution (πGE) in this problem domain. In πGE the genotype-phenotype map is changed so that the evolutionary algorithm contro...

We present two complete integer programming formulations for the ring spur assignment problem. This problem arises in the design of next generation telecommunications networks. We analyse and compare the formulations in terms of compactness, the resulting LP bound and results from a branch and cut implementation. We present our conclusions with com...

Contributions to a supply chain’s overall cost function (such as the bullwhip effect) are sensitive to the different players’ ordering policies. This chapter addresses the problem of developing ordering policies which minimise the overall supply chain cost. Evolutionary Algorithms have been used to evolve such ordering policies. The authors of this...

We present an investigation into the genotype-phenotype map in Position Independent Grammatical Evolution (πGE). Previous studies have shown πGE to exhibit a performance increase over standard Grammatical Evolution (GE). The only difference between the two approaches is in how the genotype-phenotype mapping process is performed. GE uses a leftmost...

We present an analysis of the genotype-phenotype map in Grammatical Evolution (GE). The standard map adopted in GE is a depth-first expansion of the non-terminal symbols during the derivation sequence. Earlier studies have indicated that allowing the path of the expansion to be under the guidance of evolution as opposed to a deterministic process p...

Quantum effects are a natural phenomenon and just like evolution, the brain, or immune systems, can serve as an inspiration for the design of computing algorithms. This chapter illustrates how a quantum-inspired evolutionary algorithm (QIEA) using real number encodings can be constructed and examines the utility of the resulting algorithm on an imp...

The emerging area of “Services Science” is centered on the principles of understanding of how to organize, model, implement and execute supply chains which are heavily based on human capital. A key aspect of this work has been identifying and developing concepts from traditional manufacturing-oriented supply chains and applying them to supply chain...

πGrammatical Evolution is presented and its performance on four benchmark problems is reported. πGrammatical Evolution is a position-independent variation on Grammatical Evolution’s genotype-phenotype mapping process where the order of derivation sequence steps are no longer applied to nonterminals in a predefined fashion from left to right on the...

πGrammatical Evolution is presented and its performance on four benchmark problems is reported. πGrammatical Evolution is a position-independent variation on Grammatical Evolution’s genotype-phenotype mapping process where the order of derivation sequence steps are no longer applied to nonterminals in a predefined fashion from left to right on the...

We study exterior powers of classes of symmetric bilinear forms in the Witt-Grothendieck ring of a field of characteristic not equal to 2, and derive their basic properties. The exterior powers are used to obtain annihilating polynomials for quadratic forms in the Witt ring.

Annihilating polynomials for quadratic forms in the Witt ring are obtained via an tale algebra interpretation of the Burnside ring together with a homomorphism to the Witt ring.

This research introduces a relatively new evolutionary algorithm in computer science; Grammatical Evolution (GE), to the field of supply chain dynamics and bullwhip mitigation. As a proof of concept several experiments are conducted to derive optimal ordering policies for agents in a multi-tier supply chain. These results are compared with existing...

Decision support systems (DSS) have achieved considerable success in many areas of business activity. The type of semi-structured problem where DSS is successful frequently involves the management or configuration of vehicles or machines. While DSS has been used for human resource problems such as personnel scheduling, there have been fewer DSS app...

Summary Several methods of clustering (or classifying) product components in the telecommunications industry were examined, with a view to improving methods of forecasting demand. Raw demand data were converted to time series and examined for trends/patterns. Several forecasting methods (particularly simple exponential smoothing) and measures of fo...