Tutorials.adoc

fcmaes - a Python 3 gradient-free optimization library

Quality Diversity

• Quality Diversity Parallel execution of a multi-algorithm improvement emitter and CVT MAP-Elites.

• Map Elites CVT MAP-Elites + adapted CMA-ES.

• Map Elites 2 Quality-Diversity applied to ODE based control problems.

Space Flight Trajectory Design

• Introduction Solving simple example problems from space flight dynamics.

• Space Flight Space flight mission design revisited.

• Pagmo results GTOPX Benchmark results using Pagmo

• Pykep gym results Benchmark results for the Pykep gym problems

• Mixed Integer Mixed integer flight mission design

• ESAChallenge ESA Optimization Challenge

Quantum Computing

• Variational Qubit Optimize a Variational Qubit and a Variational Quantum Eigensolver.

Service - Demand Network Planning (p-center problem)

• 5G network planning Solve the p-center-problem for irregular shapes with holes.

• Service Locations Solve the α-neighbor p-center optimization problem.

Scheduling

• Employee Scheduling Optimize an employee schedule.

• JobShop Solving the flexible job shop scheduling problem.

• Scheduling Solving a complex scheduling problem, part of the GTOC11 competition.

Routing

• One For All MMKP and VRPTW.

• Multi-UAV Multi-UAV Task Assignment.

• Routing Capacitated Vehicle Routing.

• Noisy TSP Solve the noisy Traveling Salesman Problem.

Trading

• Crypto Trading Optimize your crypto trading strategy.

Water Management

• Water Management Optimize water resource management.

Social Media

• Social Media Analyzing Social Media User Data.

Game Design

• gbea TopTrumps Benchmark TopTrumps game optimization benchmark.

Simulation

• Parameter Sweep Biochemical stochastic model parameters.

• Surrogate Optimize the Mazda car design problem.

• Robot Pushing and Navigating Rovers

• CFD Optimize CFD simulation based problems.

• Power Plant Power Plant Efficiency.

• Vaccination Modeling Vaccination.

• Hospital Managing hospital resources during a pandemic.

Machine Learning

• EvoJax Hardware-Accelerated Neuroevolution.

• Hyper Parameters Hyper parameter optimization.

Miscellaneous

• Subset selection Select an optimal subset.

• Clustering Out of the box.

• ODE Use of differential equation solvers.

fcmaes-Details

• MO-DE MO-DE, a new multi objective algorithm.

• Multi-Objective Solving multi objective problems using variable weights.

• Constraints Optimizing with constraints.

• Expressions Sequences and random choices of optimizers.

• Delayed Update Asynchronous parallel function evaluation.

Log output of the parallel retry

The log output of the parallel retry contains the following rows:

Parallel retry

• time (in sec)

• evaluations / sec

• number of retries - optimization runs

• total number of evaluations in all retries

• best value found so far

• mean of the values found by the retries below the defined threshold

• standard deviation of the values found by the retries below the defined threshold

• list of the best 20 function values in the retry store

• best solution (x-vector) found so far

Mean and standard deviation would be misleading when using coordinated retry, because of the retries initiated by crossover. Therefore the rows of the log output differ slightly:

Smart retry

• time (in sec)

• evaluations / sec

• number of retries - optimization runs

• total number of evaluations in all retries

• best value found so far

• worst value in the retry store

• number of entries in the retry store

• list of the best 20 function values in the retry store

• best solution (x-vector) found so far