By Tao Gong (auth.), Carlos A. Coello Coello, Julie Greensmith, Natalio Krasnogor, Pietro Liò, Giuseppe Nicosia, Mario Pavone (eds.)
This e-book constitutes the refereed lawsuits of the eleventh foreign convention on synthetic Immune structures, ICARIS 2012, held in Taormia, Italy, in August 2012. the nineteen revised chosen papers awarded have been rigorously reviewed and chosen for inclusion during this e-book. furthermore four papers of the workshop on bio and immune encouraged algorithms and versions for multi-level advanced platforms are incorporated during this quantity. man made immune platforms (AIS) is a various and maturing sector of study that bridges the disciplines of immunology, biology, clinical technological know-how, computing device technological know-how, physics, arithmetic and engineering. The scope of AIS levels from modelling and simulation of the immune process via to immune-inspired algorithms and in silico, in vitro and in vivo solutions.
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Additional resources for Artificial Immune Systems: 11th International Conference, ICARIS 2012, Taormina, Italy, August 28-31, 2012. Proceedings
M (2) hi (x) = 0 i = 1, 2, . . , p (3) T where x = [x1 , x2 , . . , p are the constraint functions of the problem. To describe the concept of optimality in which we are interested, we will introduce next a few deﬁnitions. Definition 1. , k, and that x dominates y (denoted by x ≺ y) if x ≤ y and x = y. Definition 2. We say that a vector of decision variables x ∈ X ⊂ IRn is nondominated with respect to X , if there does not exist another x ∈ X such that f (x ) ≺ f (x). Definition 3. We say that a vector of decision variables x∗ ∈ F ⊂ IRn (F is the feasible region) is Pareto-optimal if it is nondominated with respect to F .
An archive is deﬁned (line 2) in order to store the nondominated solutions found so far. The main (or internal) population is initialized (line 3) containing the solutions from the current generation. The main loop starts and performs the following steps until a stop criterion is met. The algorithm evaluates the online (or main) population (line 5) using the objective functions and constraints of the problem. Depending on the choices made, the solutions of the set B are analyzed and given an aﬃnity value (line 6), the archive A can be used, for example, to deﬁne the new aﬃnities between the current solutions and the best solutions found so far.
For each N C solution, the number of clones of each candidate (N CC) is given by: N CC(Ai,j ) = Pj ∗ Af f (Ai,j ) ∀i, j Af f (Ai,j ) nj i=0 where: Ai,j is the ith antigen or the ith antibody of the set j, Pj is the total number of clones for the set j, nj is the number of candidates in the set j. 6. Mutation Regarding mutation, two important choices have to be made: – Mutation probability: It controls the probability to mutate one variable of a vector. – Mutation step-size: It controls the degree of perturbation given to the variable selected to be mutated.