Introduction to Information-Geometric Optimization

Information-Geometric Optimization Cybozu Labs Study Session - GA - evolutionary strategy

- optimization problem

• No requirement that the objective function be differentiable
  • NN is a bundle of differentiable functions
• Interpret genetic algorithms as updating probability distributions

• The general gradient method differentiates the objective function f on X-space and updates it using the gradient
• Objective function is not differentiable
• Let's make a differentiable objective function g on the parameter interval

• Means of creating functions on parameter space
• Example: Average
• IGO "I don't like this."

---- before any additions were made during the study session.

- GA - evolutionary strategy - optimization problem

• No requirement that the objective function be differentiable
  • NN is a bundle of differentiable functions
• Interpret genetic algorithms as updating probability distributions
• The general gradient method differentiates the objective function f on X-space and updates it using the gradient
• Objective function is not differentiable
• Let's make a differentiable objective function g on the parameter interval
• Means of creating functions on parameter space
• Example: Average
• IGO "I don't like this."

--- Preparation of documents

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