anchor.diff:  + 
   The default algorithms will already easily surpass human planners and most in-house implementations.
   Use the <<benchmarker,Benchmarker>> to <<powerTweaking,power tweak>> to get even better results.
  -
  
positive.code:     <!--<scoreDrl>org/optaplanner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
    </scoreDirectorFactory>
  ----
  . **Optimization algorithms configuration** 
  
  *How should Planner optimize it?*
  +
  In this case, we use the default <<optimizationAlgorithms,optimization algorithms>> (because no explicit optimization algorithms are configured) for 30 seconds:
  +
  
  [source,xml,options="nowrap"]
  ----
    <termination>
  
negative.code: 
  This solver configuration consists of three parts:
  
  . *Domain model configuration*: _What can Planner change?_
  +
  We need to make Planner aware of our domain classes. In this configuration, it will automatically scan all classes in your classpath (for a `@PlanningEntity` or `@PlanningSolution` annotation):
  +
  [source,xml,options="nowrap"]
  ----
    <scanAnnotatedClasses/>
  ----
  
  . *Score configuration*: _How should Planner optimize the planning variables? What is our goal?_
  +
  Since we have hard and soft constraints, we use a ``HardSoftScore``. But we need to tell Planner how to calculate the score, depending on our business requirements. Further down, we will look into two alternatives to calculate the score: using an easy Java implementation, or using Drools DRL.
  +
  