Check Pareto Dominance Takes two individuals and returns the dominance relation, assuming a minimization problem. | |

Compute Diversity Computes the paremetric diversity of each solution in the list and adds it as an objective | |

Construct Solution Takes a lists of numbers for parameters and/or objectives to wrap them in a single object for better handling of pools of solutions [generations etc.] | |

Cull Duplicate Solutions Removes duplicate solutions | |

Deconstruct Solution Takes an OctopusSolution object and explodes it into parameters and objectives | |

Cull Elite Selects an elite of best multi-objective solutions, assuming a minimization problem. | |

Cull Pareto Fronts (Pareto Fronts) Divides a set of solutions into pareto-fronts, assuming a minimization problem. | |

Hypervolume Contributions (HV+) Calculates the Hypervolume contributions of a multi-dimensional set of points in relation to a reference point | |

Hypervolume Calculates the Hypervolume of the ParetoFront of a multi-dimensional set of points; exact algorithm; normalizes the pareto front to objectives between 0 and 1 | |

Mutate (Mutate an Octopus Solution) Mutate a solution's parameter values | |

Crossover (Simulated Binary Crossover) Takes two individuals and exchanges parameters between them - after 'SBX - Simulated Binary Crossover' | |

Tournament Selection (TS) Tournament Selection for single or multi objective solutions, assuming a minimization problem. | |

Remap Objectives Remaps the objective values of a set of solutions, assuming a minimization problem when taking the pareto fron as a start domain. |

Breeder Settings - All (oEL) (SetAll (octEvoLearn)) Settings for the NEAT Algorithm to evolve an ANN | |

Breeder Settings - Basics (oEL) (SetBase (octEvoLearn)) Basic Settings for the NEAT Algorithm to evolve an ANN. These will override any changes made to the properties in the All-Settings Component. | |

Deconstruct Network (oEL) (DeconN (octEvoLearn)) Deconstruct a network into its nodes, connections, weights, functions and metadata like performance data. | |

Deconstruct Network Obj (oEL) (DeconNObj (octEvoLearn)) Gives the Network's Objective and Fitness Values | |

Evaluate Network (oEL) (EvalN (octEvoLearn)) Forward-Pass through the Network: Takes values for the input nodes and calculates the outputs. | |

Field Curve (oEL) (FC (octEvoLearn)) Draws a curve following the direction field defined by a Network. Integration with Runge-Kutta 4th order. | |

Modify Weights (oEL) (Crossover (octEvoLearn)) Takes a pool of Networks and produces offspring by crossover mating | |

Mutate Weights (oEL) (MutW (octEvoLearn)) Mutate connection weights of a Network | |

Breeder (oEL) (NEAT (octEvoLearn)) Evolves artificial neural networks with the NEAT algorithm, using SharpNeatLib by Sebastian Risi | |

Random Network (oEL) (RN (octEvoLearn)) Generate a random network | |

Show Network (oEL) (SN (octEvoLearn)) Opens a window to show the Network | |

Construct Network (oEL) (ConsN (octEvoLearn)) Create a Network by node-points, connection-indices and weights. |

Network Training Settings (oSL) (NTS (octSupervLearn)) Settings for RPROP Supervised Learning of an ANN | |

Network Evaluate (oSL) (EvalN (octSupervLearn)) Evaluate a network for some input values | |

Network Learning (oSL) (NetLearn (octSupervLearn)) Supervised example training of a Network by multi-core resilient propagation algorithm, using the Encog library by Jeff Heaton | |

SVM Evaluate (oSL) (SVMEval (octSupervLearn)) Evaluate the learnt SVM function | |

SVM Learning (oSL) (SVMLearn (octSupervLearn)) Train SVM and optionally estimate parameters using grid search and cross validation |

Octopus Loop Octopus Loop | |

Octopus Evaluator (Octopus Eval) Octopus Evaluator |

MD NearestNeighbors (MD NearN) Neighbourhood search of multi-dimensional points (euclidean kd-tree) |

Select Solutions Takes octopus solutions or networks with saved phenotype meshes to show them for selection |

Octopus Multi-objective seach and optimzation |

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