| Evolutionary computation / 1： |
| Natural and artificial evolution / 1.1： |
| The classical paradigms / 1.2： |
| Genetic programming / 1.3： |
| Why yet another one evolutionary optimizer? / 2： |
| Background / 2.1： |
| Where to draw the lines / 2.2： |
| Individuals / 2.3： |
| Problem specification / 2.4： |
| Coding Techniques / 2.5： |
| The ?Gp architecture / 3： |
| Conceptual design / 3.1： |
| The evolutionary core / 3.2： |
| Evolutionary Operators / 3.2.1： |
| Population / 3.2.2： |
| The Evolutionary Cycle / 3.3： |
| Genetic operator selection / 3.3.1： |
| Parents selection / 3.3.2： |
| Offspring Generation / 3.3.3： |
| Individual Evaluation and Slaughtering / 3.3.4： |
| Termination and Aging / 3.3.5： |
| Advanced features / 4： |
| Self adaptation for exploration or exploitation / 4.1： |
| Self-adaptation inertia / 4.1.1： |
| Operator strength / 4.1.2： |
| Tournament size / 4.3.3： |
| Escaping local optimums / 4.2： |
| Operator activation probability / 4.2.1： |
| Tuning the elitism / 4.2.2： |
| Preserving diversity / 4.3： |
| Clone detection, scaling and extermination / 4.3.1： |
| Entropy and delta-entropy computation / 4.3.2： |
| Fitness holes |
| Population topology and multiple populations |
| Coping with the real problems / 4.4： |
| Parallel fitness evaluation / 4.4.1： |
| Multiple fitness / 4.4.2： |
| Performing an evolutionary run / 5： |
| Robot Pathfinder / 5.1： |
| ?Gp Settings / 5.2： |
| Population Settings / 5.3： |
| Library of Constraints / 5.4： |
| Launching the experiment / 5.5： |
| ?Gp Extractor / 5.6： |
| Command line syntax / 6： |
| Starting a run / 6.1： |
| Controlling messages to the user / 6.2： |
| Getting help and information / 6.3： |
| Controlling logging / 6.4： |
| Controlling recovery / 6.5： |
| Controlling evolution / 6.6： |
| Controlling evaluation / 6.7： |
| Syntax of the settings file / 7： |
| Syntax of the population parameters file / 7.1： |
| Strategy parameters / 8.1： |
| Base parameters / 8.1.1： |
| Parameters for self adaptation / 8.1.2： |
| Other parameters / 8.1.3： |
| Syntax of the external constraints file / 9： |
| Purposes of the constraints / 9.1： |
| Organization of constraints and hierarchy / 9.2： |
| Specifying the structure of the individual / 9.3： |
| Specifying the contents of the individual / 9.4： |
| Writing a compliant evaluator / 10： |
| Information from ?Gp to the fitness evaluator / 10.1： |
| Expected fitness format / 10.2： |
| Good Examples / 10.2.1： |
| Bad Examples / 10.2.2： |
| Implementation details / 11： |
| Design principles / 11.1： |
| Architectural choices / 11.2： |
| The Graph library / 11.2.1： |
| The Evolutionary Core library / 11.2.2： |
| Front end / 11.2.3： |
| Code organization and class model / 11.3： |
| Examples and applications / 12： |
| Classical one-max / 12.1： |
| Fitness evaluator / 12.1.1： |
| Constraints / 12.1.2： |
| Population settings / 12.1.3： |
| ?Gp settings / 12.1.4： |
| Running / 12.1.5： |
| Values of parameters and their influence on the evolution: Arithmetic expressions / 13412.2： |
| De Jong 3 / 12.2.1： |
| De Jong 4-Modified / 12.2.2： |
| Carrom / 12.2.3： |
| Complex individuals' structures and evaluation: Bit-counting in Assembly / 12.3： |
| Assembly individuals representation / 12.3.1： |
| Evaluator / 12.3.2： |
| Argument and option synopsis / 12.3.3： |
| External constraints synopsis |
| References |
| Evolutionary computation / 1： |
| Natural and artificial evolution / 1.1： |
| The classical paradigms / 1.2： |
ＥＢ
