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(The unit of addition is zero (that is, x+Q = x for all x), the unit of multiplication is one (xxl=x for all x), the unit of conjunction is true (that is, x A true = x), and so on Minimum and maximum can be given units by adding so-called 'fictitious' values, respectively <x> and -oo, to their domains but care has to be taken in defining how these values behave in combination with other arithmetic operators The unit of highest common factor is the number 0, so long as its domain is not restricted to only strictly positive numbers) We use a uniform notation to denote quantifications over a number of values In this way, we can also present a uniform set of laws for manipulating quantifiers, resulting in a substantial reduction in the number of laws one has to remember.

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[Dorigo 1999] M Dorigo, Artificial Life: The Swarm Intelligence Approach, Tutorial TD1, Congress on Evolutionary Computing, Washington, DC, 1999 [Dorigo and Di Caro 1999] M Dorigo, G Di Caro, The Ant Colony Optimization Meta-Heuristic, in D Corne, M Dorigo, F Glover (eds), New Ideas in Optimization, McGraw-Hill, 1999 [Dorizzi et al 1996] B Dorizzi, G Pellieux, F Jacquet, T Czernichow, A Munoz, Variable Selection using Generalized RBF Networks: Application to the Forecast of the French T-Bonds, Proceedings of Computational Engineering in Systems Applications, Lille, France, 1996, pp 122 127 [Drucker 1999] H Drucker, Boosting using Neural Networks, in A Sharkey (ed), Combining Artificial Neural Nets, Perspectives in Neural Computing, Springer, 1999, pp 51 78 [Duch and Korczak 1998] W Duch, J Korczak, Optimization and Global Minimization Methods Suitable for Neural Networks, Neural Computing Surveys, Vol 2, 1998.

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Figure 1.12 The one-dimensional potentials V(z) in the conduction and valence bands for a typical Type-I single quantum well (left) compared to a Type-II system (right)

We begin by explaining the particular case of summation, comparing our notation with the Sigma notation discussed above Then, we consider quantifications with respect to conjunction and disjunction ('for all' quantifications and 'there exist' quantifications, respectively) before considering the general case..

localised in the same region of space, which makes for efficient (fast) recombination. However other possibilities can exist, as illustrated in Fig. 1.12.

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[Durbin and Rumelhart 1989] R Durbin, DE Rumelhart, Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks, Neural Computation, Vol 1, 1989, pp 133 142 [Durham 1994] W Durham, Co-Evolution: Genes, Culture, and Human Diversity, Stanford University Press, 1994 [Eberhart et al 1996] RC Eberhart, RW Dobbins and P Simpson, Computational Intelligence PC Tools, Academic Press, 1996 [Eberhart and Kennedy 2001] RC Eberhart, J Kennedy, Swarm Intelligence, Morgan Kaufmann, 2001 [Engelbrecht et al 1995a] AP Engelbrecht, I Cloete, J Geldenhuys, JM Zurada, Automatic Scaling using Gamma Learning for Feedforward Neural Networks, International Workshop on Artificial Neural Networks, Torremolinos, Spain, June 1995, in J Mira, F Sandoval (eds), From Natural to Artificial Neural Computing, in the series Lecture Notes in Computer Science, Vol 930, pp 374 381 [Engelbrecht et al.

Our notation for summation has the form (Lbv : range : term) . There are five components to the notation, which we explain in turn. The first component is the quantifier, in this case Z. By a long-standing convention among mathematicians, 2 denotes summation of some arbitrary number of values. The second component is the dummy bv. The dummy is said to be bound to the quantifier; it is also called the bound variable. We use identifiers like i, j and k, or x, y and z as dummies. Later, we allow the possibility of a list of dummies rather than just a single one. The third component is the range of the dummy. The range is a boolean-valued expression that determines a set of values of the dummy: specifically, the set of all values of the bound variable for

Figure 1.13 The one-dimensional potentials V(z) in the conduction and valence bands for a typical Type-I superlattice (left) compared to a Type-II system (right) In Type-II systems the bandgaps of the materials, say 'A' and 'C', are aligned such that the quantum wells formed in the conduction and valence bands are in different materials, as illustrated in Fig. 1.12 (right). This leads to the electrons and holes being confined in different layers of the semiconductor. The consequence of this is that the recombination times of electrons and holes are long.

1.7 THE ENVELOPE FUNCTION APPROXIMATION Two important points have been argued: 1. The effective mass approximation is a valid description of bulk materials.

^Recall that a unit of a symmetric binary operator is a value 1 satisfying 1 x = x, for all x.

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