Evolutionary optimization for computationally expensive problems.

TL;DR: Key variables which influence the surrogate performance, as applied to the aerodynamic optimization of an aerofoil, such as the database size, the number of geometric design parameters, variation from the baseline geometry, Reynolds number and angle of attack are identified and analyzed.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.