This book provides theoretical and practical knowledge for develop- ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod- els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight...
This book provides theoretical and practical knowledge for develop- ment of algorithms that infer linear and nonlinear models. It offers a methodology...
This book provides theoretical and practical knowledge for develop- ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod- els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight...
This book provides theoretical and practical knowledge for develop- ment of algorithms that infer linear and nonlinear models. It offers a methodology...
"Practical Applications of Evolutionary Computation to Financial Engineering" presents the state of the art techniques in Financial Engineering using recent results in Machine Learning and Evolutionary Computation. This book bridges the gap between academics in computer science and traders and explains the basic ideas of the proposed systems and the financial problems in ways that can be understood by readers without previous knowledge on either of the fields. To cement the ideas discussed in the book, software packages are offered that implement the systems described within.
The...
"Practical Applications of Evolutionary Computation to Financial Engineering" presents the state of the art techniques in Financial Engineering usi...
Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists
This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested...
Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computati...
This book bridges the gap between computer science academics and traders, presenting state-of-the-art techniques in financial engineering using machine learning and evolutionary computation. Includes information on software for implementing solutions.
This book bridges the gap between computer science academics and traders, presenting state-of-the-art techniques in financial engineering using machin...
Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization.
Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and...
Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilit...
