diff --git a/eo/tutorial/html/eoLesson4.html b/eo/tutorial/html/eoLesson4.html index 541c2305..0ddab737 100644 --- a/eo/tutorial/html/eoLesson4.html +++ b/eo/tutorial/html/eoLesson4.html @@ -24,24 +24,42 @@ documentation

-Tutorial Lesson 4: fully operational EA

+Tutorial Lesson 4: ready-to-use fully operational EA In this lesson, you will still use the same Evolutionary Algorithm. But this time you will have full control of all components -from the command-line or a parameter file. -You can even use the algorithm decribed here without any other knowledge -of EO, just by writing your fitness function as a plain C++ function. This -is why this lesson starts with a user's guide, -most of it being representation-independent, with some parts that are specific -of respectively the binary and the real -algorithms. -
However, the ultimate purpose of this tutorial is to be able to do +from the command-line or a parameter file.
+You can even use the algorithm decribed here without any other knowledge +of EO, just by writing your fitness function as a plain C++ function.

+ +Contents
+ +


User's @@ -462,7 +480,16 @@ operators).

User's guide:Real-valued specific parameters -
The following describes the specific parameters that are available +
+To run your own real-valued application, write your fitness function +(see real_value.h), +recompile, and run from the command line
+

RealEA @RealEA.param

+in order to use sensible parameters! (see Lesson 3 +for details on the parameter file). +But remember that Self-adaptive ES will work much better! +

+The following describes the specific parameters that are available in programs RealEA and ESEA to evolve genotypes that are vector<double>. @@ -634,8 +661,16 @@ The value of standard deviation for Gaussian mutation - fixed along evolution

User's guide:ES -with self-adative mutation specific parameters -
The following describes the specific parameters for program ESEA, +with self-adative mutation parameters +
+To run your own SA-ES application, write your fitness function +(see real_value.h), +recompile, and run from the command line
+

ESEA @ESEA.param

+in order to use sensible parameters! (see Lesson 3 +for details on the parameter file). +

+The following describes the specific parameters for program ESEA, that implements the full Evolution-Strategy self-adaptive mutation mechanism - together with specific ES crossover operators. The initialization section is the same as the one for plain vector<double> above, so only the opeartor diff --git a/eo/tutorial/html/eoProgramming.html b/eo/tutorial/html/eoProgramming.html index 7889a499..40ffcad3 100644 --- a/eo/tutorial/html/eoProgramming.html +++ b/eo/tutorial/html/eoProgramming.html @@ -43,9 +43,11 @@ EO<F>

and then use it in your application as

eoBit<double> myeoBit;

declares an object of type eoBin which has as fitness a double. -

Whereas the advantages are obvious (writing generic reusable code instead +

Whereas the advantages +are obvious (writing generic reusable code instead of having to rewrite the same pieces of code for different types), there -are some drawbacks: namely, it makes some of the compiler error messages +are some drawbacks: +namely, it makes some of the compiler error messages hard to understand; and it forbids the compilation of most parts of EO into an object library file, as the actual types are not known in advance.

diff --git a/eo/tutorial/html/eoTutorial.html b/eo/tutorial/html/eoTutorial.html index 077bfe8f..53bfedf6 100644 --- a/eo/tutorial/html/eoTutorial.html +++ b/eo/tutorial/html/eoTutorial.html @@ -28,7 +28,7 @@ if you are looking for a ready-to-use (except for the fitness) fully tunable Evolutionary Algorithm evolving real values or bitstring, you can go directly to -Lesson 5 after +Lesson 4 after just reading this page, and maybe the Programming hints (link on top of each page too).
In fact, there is something new: thanks to Jochen Küpper, EO automatic configuration