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Article
Coupled Dynamics of Fecal Indicator Bacteria in Sandy Sediments and the Water Column: a 3-Year High-Frequency Study at a Pennsylvania Creek
Elevated concentrations of the fecal indicator bacteria (FIB) Escherichia coli and enterococci in recreation and irrigation waters indicate a human health risk. Little is known about the role of sandy bottom sedi...
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Article
Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA
Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and ass...
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Chapter
Nonstationary and Noisy Function Optimisation
Unlike most of the examples we have used so far, real-world environments typically contain sources of uncertainty. This means that if we measure the fitness of a solution more than once, we will not always get...
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Chapter
Constraint Handling
In this chapter we return to an issue first introduced in Sect. 1.3, namely that some problems have constraints associated with them. This means that not all possible combinations of variable values represent ...
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Chapter
Coevolutionary Systems
In most of this book we have been concerned with problems where the quality of a proposed solution can be relatively easily measured in isolation by some externally provided fitness function. Evaluating a solu...
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Chapter
Parameters and Parameter Tuning
Chapter 3 presented an algorithmic framework that forms the common basis for all evolutionary algorithms. A decision to use an evolutionary algorithm implies that the user adopts the main design decisions behi...
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Chapter
Evolutionary Computing: The Origins
This chapter provides the reader with the basics for studying evolutionary computing (EC) through this book. We begin with a brief history of the field of evolutionary computing, followed by an introduction to...
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Chapter
Multiobjective Evolutionary Algorithms
In this chapter we describe the application of evolutionary techniques to a particular class of problems, namely multiobjective optimisation. We begin by introducing this class of problems and the particularly...
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Chapter
Interactive Evolutionary Algorithms
This chapter discusses the topic of interactive evolution, where the measure of a solution’s fitness is provided by a human’s subjective judgement, rather than by some predefined model of a problem. Of course, th...
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Chapter
Problems to Be Solved
In this chapter we discuss problems to be solved, as encountered frequently by engineers, computer scientists, etc. We argue that problems and problem solvers can, and should, be distinguished, and observe tha...
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Chapter
What Is an Evolutionary Algorithm?
The most important aim of this chapter is to describe what an evolutionary algorithm (EA) is. In order to give a unifying view we present a general scheme that forms the common basis for all the different vari...
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Chapter
Fitness, Selection, and Population Management
As explained in Chap. 3, there are two fundamental forces that form the basis of evolutionary systems: variation and selection. In this chapter we discuss the EA components behind the second one. Having discus...
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Chapter
Evolutionary Robotics
In this chapter we discuss evolutionary robotics (ER), where evolutionary algorithms are employed to design robots. Our emphasis lies on the evolutionary aspects, not on robotics per se. Therefore, we only bri...
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Chapter
Working with Evolutionary Algorithms
In this chapter we discuss the practical aspects of using EAs. Working with EAs often means comparing different versions experimentally, and we provide guidelines for doing this, including the issues of algori...
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Chapter
Hybridisation with Other Techniques: Memetic Algorithms
In the preceding chapters we described the main varieties of evolutionary algorithms and described various examples of how they might be suitably implemented for different applications. In this chapter we turn...
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Chapter
Representation, Mutation, and Recombination
As explained in Chapt. 3, there are two fundamental forces that form the basis of evolutionary systems: variation and selection. In this chapter we discuss the EA components behind the first one. Since variati...
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Chapter
Popular Evolutionary Algorithm Variants
In this chapter we describe the most widely known evolutionary algorithm variants. This overview serves a twofold purpose: On the one hand, it introduces those historical EA variants without which no EC textbo...
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Chapter
Theory
In this chapter we present a brief overview of some of the approaches taken to analysing and modelling the behaviour of evolutionary algorithms. The Holy Grail of these efforts is the formulation of predictive...
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Chapter
Parameter Control
The issue of setting the values of evolutionary algorithm parameters before running an EA was treated in the previous chapter. In this chapter we discuss how to do this during a run of an EA, in other words, we e...
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Book
