# TEgoSolver

There are 4 available solvers combining two distinct features:

- dynamic or static optimization: in static, the search restart with a new random population; in 
dynamic the search restart from the previous population which should be rich enough to find a point 
that was put aside. In dynamic, the first search is longer than the following one.

- genetic or HJMA algorithm;

This leads to the following classes: `TEgoDynSolver`, `TEgoStdSolver`, `TEgoHjDynSolver` and 
`TEgoHjStdSolver`

some methods are provided:

- `significantEI` with a `double` parameter is used to define the EI threshold to stop the search 
loop

- `setManyNewItem` with an `int` is used to define the maximum number of new items used to feed the 
empty resources (unused with TEgoStdSolver).

- for the class using HJMA, `setSize` with two `int` parameters defines the number of global search 
performed by the optimization in the preliminary search and in the following ones.

- for the class using Vizir algorithm, `setSolver` with a pointer on a `TVizirSolver` defines the 
solver to use.

- for the `TEgoDynSolver`, the first and longer search uses the maximum evaluation number defined in 
the solver. The following search are shorter and is defined using `setStepSize` and its `int` argument.