A nature-inspired route through adaptive systems: neural learning, evolutionary computation, swarm intelligence, artificial immune models, fuzzy systems, and rough sets. Every finished chapter is a detailed, story-led master's companion with a first-principles map, central derivation, worked case, failure modes, fair evaluation plan, study lab, oral-exam questions, and complete summary.
Weighted net input, bias, activation functions, geometric decision boundaries, augmented vectors, gradient descent, Widrow-Hoff, generalized delta, and error-correction learning.
chapter 3Feedforward, functional-link, product-unit, recurrent, time-delay, and cascade networks; supervised objectives; gradient and alternative optimization; hidden units; and ensembles.
chapter 4Hebbian learning, principal-component learning, learning-vector quantization, self-organizing feature maps, stochastic and batch training, growing maps, clustering, and visualization.
chapter 5Learning-vector quantization II, radial basis architecture, Gaussian and alternative basis functions, center and width selection, output-weight fitting, and network variations.
chapter 6Learning through rewards, model-free reinforcement learning, temporal-difference learning, Q-learning, neural value approximation, RPROP, gradient approaches, and connectionist Q-learning.
chapter 7Accuracy, complexity, convergence, generalization, data preparation, robust losses, initialization, learning rate, momentum, optimizer and architecture choice, adaptive activations, and active learning.
Generic evolutionary algorithms, chromosome representation, initialization, fitness, selection pressure, reproduction operators, stopping conditions, elitism, and comparison with classical optimization.
chapter 9Canonical genetic algorithms, binary and floating-point crossover, mutation, control parameters, steady-state and island variants, messy GAs, niching, constraints, multi-objective and dynamic optimization.
chapter 10Tree-based program representation, function and terminal sets, population initialization, fitness, subtree crossover and mutation, closure, bloat, building blocks, applications, and interpretable symbolic search.
chapter 11Basic evolutionary programming, mutation-centered search, probabilistic selection, static and dynamic strategy parameters, self-adaptation, distribution variants, local-search hybrids, constraints, and applications.
chapter 12(1+1)-ES, (μ,λ) and (μ+λ) selection, recombination, mutation, strategy parameters, covariance and directed variation, self-adaptation, surrogate models, constraints, noise, and niching.
chapter 13Difference-vector mutation, target, base, and donor vectors, binomial and exponential crossover, greedy selection, DE/x/y/z notation, parameter control, hybrids, discrete variants, and advanced optimization settings.
chapter 14Population and belief spaces, acceptance and influence functions, normative, situational, domain, topographic, and historical knowledge, fuzzy cultural algorithms, constraints, dynamic and multi-objective problems.
chapter 15Competitive and cooperative coevolution, relative fitness, arms races, cycling, disengagement, collaborator selection, problem decomposition, credit assignment, and applications.
Global-best and local-best PSO, cognitive and social velocity components, neighborhood topologies, inertia, velocity clamping, constriction, update timing, convergence, hybrid, binary, niching, dynamic, and multi-objective PSO.
chapter 17Stigmergy, artificial pheromone, ant-system construction, evaporation, Ant Colony System, Max-Min Ant System, Ant-Q, rank variants, clustering, division of labor, continuous and multi-objective ACO.
The classical immune view, antibodies and antigens, lymphocytes and other white cells, innate and adaptive immunity, recognition, clonal learning, memory, network theory, and danger theory as computational inspiration.
chapter 19Generic AIS design, negative selection, evolutionary detectors, clonal selection and CLONALG, dynamic and multilayered systems, immune networks, self-stabilization, danger models, anomaly detection, clustering, and applications.
Crisp and fuzzy membership, membership functions, support, core, height, α-cuts, containment, complement, t-norm and t-conorm operators, and the distinction between fuzziness and probability.
chapter 21Linguistic variables, hedges, fuzzy propositions and rules, implication, fuzzification, rule activation, aggregation, approximate inference, defuzzification, and interpretable rule-base design.
chapter 22Fuzzy-control components, knowledge bases, input and output scaling, table-based, Mamdani, and Takagi-Sugeno controllers, stability, tuning, rule surfaces, implementation, and evaluation.
chapter 23Indiscernibility relations, information systems, lower and upper approximations, boundary regions, positive and negative regions, approximation accuracy, reducts, dependency, and rough versus fuzzy uncertainty.