|Check Pareto Dominance|
Takes two individuals and returns the dominance relation, assuming a minimization problem.
Computes the paremetric diversity of each solution in the list and adds it as an objective
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
Takes an OctopusSolution object and explodes it into parameters and objectives
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
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
Remaps the objective values of a set of solutions, assuming a minimization problem when taking the pareto fron as a start domain.
|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.
|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.
|Construct Network (oEL) (ConsN (octEvoLearn))|
Create a Network by node-points, connection-indices and weights.
|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
|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
|MD NearestNeighbors (MD NearN)|
Neighbourhood search of multi-dimensional points (euclidean kd-tree)
Takes octopus solutions or networks with saved phenotype meshes to show them for selection
Multi-objective seach and optimzation