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This is more of a workaround than a direct solution, but a common way to avoid local minima is to run your algorithm several times, from different starting locations. You can then take the best outcome or the average as your final result.

The reason why you might want to take the average rather than the best is to avoid overfitting. Many model types where local minima are a problem have lots of parameters: decision trees, neural networks, etc. Simply taking the best outcome risks obtaining a model that won't generalise well to future data. Taking the average guards against this.

You can get into arbitrarily complex ways of doing the averaging. Have a look at

This is more of a workaround than a direct solution, but a common way to avoid local minima is to run your algorithm several times, from different starting locations. You can then take the best outcome or the average as your final result.

The reason why you might want to take the average rather than the best is to avoid overfitting. Many model types where local minima are a problem have lots of parameters: decision trees, neural networks, etc. Simply taking the best outcome risks obtaining a model that won't generalise well to future data. Taking the average guards against this.

You get into arbitrarily complex ways of doing the averaging. Have a look at

This is more of a workaround than a direct solution, but a common way to avoid local minima is to run your algorithm several times, from different starting locations. You can then take the best outcome or the average as your final result.

The reason why you might want to take the average rather than the best is to avoid overfitting. Many model types where local minima are a problem have lots of parameters: decision trees, neural networks, etc. Simply taking the best outcome risks obtaining a model that won't generalise well to future data. Taking the average guards against this.

You can get into arbitrarily complex ways of doing the averaging. Have a look at

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source | link

This is more of a workaround than a direct solution, but a common way to avoid local minima is to run your algorithm several times, from different starting locations. You can then take the best outcome or the average as your final result.

The reason why you might want to take the average rather than the best is to avoid overfitting. Many model types where local minima are a problem have lots of parameters: decision trees, neural networks, etc. Simply taking the best outcome risks obtaining a model that won't generalise well to future data. Taking the average guards against this.

You get into arbitrarily complex ways of doing the averaging. Have a look at