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How to interpret hill-climbing graphs?
How do you change the size of figures drawn with matplotlib?Hill climbing and single-pair shortest path algorithmsHill climbing algorithm simple exampleHill-climbing algorithm to generate a string using Levenshtein distance as heuristic in Python?Proper Heuristic Mechanism For Hill ClimbingStochastic hill climbing vs first-choice hill climbing algorithmsWhat is the difference between Stochastic Hill Climbing and First Choice Hill Climbing?Matlab plot removing space around graphHow to create a hill climbing algorithmStochastic hill climbing vs random-restart hill climbing algorithms
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When viewing hill-climbing graphs I'm unsure of how to interpret the x-axis. My current understanding is that points on the y-axis are a result of how good the current state is, and the x-axis consists of every possible state.
What I'm unsure about however is how the x-axis / ordering of the x-axis is determined.
First thought: Iterate through the full state space, evaluating each state and plotting points.
But, this isn't actual hill-climbing...
Second thought: The hill-climbing algorithm has been executed and each best neighbour has been plotted, left to right in turn along x-axis.
But, how would these steady down slopes ever be produced? Would random restarting not produce jagged edges?
Third thought: These downslopes are a result of back tracking.
But, finally, why would every graph not result in the right-most point being the peak, indicating that the goal state was found? Are these graphs only ever referring to optimisation problems that simply time out or reach their max iterations?
Where am I going wrong in my thought process here? Are these graphs simply abstract and I'm over thinking it? Any guidance would be greatly appreciated.
graph artificial-intelligence diagram backtracking hill-climbing
add a comment |
When viewing hill-climbing graphs I'm unsure of how to interpret the x-axis. My current understanding is that points on the y-axis are a result of how good the current state is, and the x-axis consists of every possible state.
What I'm unsure about however is how the x-axis / ordering of the x-axis is determined.
First thought: Iterate through the full state space, evaluating each state and plotting points.
But, this isn't actual hill-climbing...
Second thought: The hill-climbing algorithm has been executed and each best neighbour has been plotted, left to right in turn along x-axis.
But, how would these steady down slopes ever be produced? Would random restarting not produce jagged edges?
Third thought: These downslopes are a result of back tracking.
But, finally, why would every graph not result in the right-most point being the peak, indicating that the goal state was found? Are these graphs only ever referring to optimisation problems that simply time out or reach their max iterations?
Where am I going wrong in my thought process here? Are these graphs simply abstract and I'm over thinking it? Any guidance would be greatly appreciated.
graph artificial-intelligence diagram backtracking hill-climbing
The x-axis in such drawings is usually an arbitrary and conceptual cross-section through a high-dimensional optimisation 'surface'. That's all. It's a picture to illustrate a concept. Yes, as you put it, you are over-thinking it.
– High Performance Mark
Mar 23 at 7:55
Okay great this makes a lot of sense, thank you for the clarification!
– blewittrb
Mar 23 at 12:34
add a comment |
When viewing hill-climbing graphs I'm unsure of how to interpret the x-axis. My current understanding is that points on the y-axis are a result of how good the current state is, and the x-axis consists of every possible state.
What I'm unsure about however is how the x-axis / ordering of the x-axis is determined.
First thought: Iterate through the full state space, evaluating each state and plotting points.
But, this isn't actual hill-climbing...
Second thought: The hill-climbing algorithm has been executed and each best neighbour has been plotted, left to right in turn along x-axis.
But, how would these steady down slopes ever be produced? Would random restarting not produce jagged edges?
Third thought: These downslopes are a result of back tracking.
But, finally, why would every graph not result in the right-most point being the peak, indicating that the goal state was found? Are these graphs only ever referring to optimisation problems that simply time out or reach their max iterations?
Where am I going wrong in my thought process here? Are these graphs simply abstract and I'm over thinking it? Any guidance would be greatly appreciated.
graph artificial-intelligence diagram backtracking hill-climbing
When viewing hill-climbing graphs I'm unsure of how to interpret the x-axis. My current understanding is that points on the y-axis are a result of how good the current state is, and the x-axis consists of every possible state.
What I'm unsure about however is how the x-axis / ordering of the x-axis is determined.
First thought: Iterate through the full state space, evaluating each state and plotting points.
But, this isn't actual hill-climbing...
Second thought: The hill-climbing algorithm has been executed and each best neighbour has been plotted, left to right in turn along x-axis.
But, how would these steady down slopes ever be produced? Would random restarting not produce jagged edges?
Third thought: These downslopes are a result of back tracking.
But, finally, why would every graph not result in the right-most point being the peak, indicating that the goal state was found? Are these graphs only ever referring to optimisation problems that simply time out or reach their max iterations?
Where am I going wrong in my thought process here? Are these graphs simply abstract and I'm over thinking it? Any guidance would be greatly appreciated.
graph artificial-intelligence diagram backtracking hill-climbing
graph artificial-intelligence diagram backtracking hill-climbing
asked Mar 23 at 7:47
blewittrbblewittrb
360413
360413
The x-axis in such drawings is usually an arbitrary and conceptual cross-section through a high-dimensional optimisation 'surface'. That's all. It's a picture to illustrate a concept. Yes, as you put it, you are over-thinking it.
– High Performance Mark
Mar 23 at 7:55
Okay great this makes a lot of sense, thank you for the clarification!
– blewittrb
Mar 23 at 12:34
add a comment |
The x-axis in such drawings is usually an arbitrary and conceptual cross-section through a high-dimensional optimisation 'surface'. That's all. It's a picture to illustrate a concept. Yes, as you put it, you are over-thinking it.
– High Performance Mark
Mar 23 at 7:55
Okay great this makes a lot of sense, thank you for the clarification!
– blewittrb
Mar 23 at 12:34
The x-axis in such drawings is usually an arbitrary and conceptual cross-section through a high-dimensional optimisation 'surface'. That's all. It's a picture to illustrate a concept. Yes, as you put it, you are over-thinking it.
– High Performance Mark
Mar 23 at 7:55
The x-axis in such drawings is usually an arbitrary and conceptual cross-section through a high-dimensional optimisation 'surface'. That's all. It's a picture to illustrate a concept. Yes, as you put it, you are over-thinking it.
– High Performance Mark
Mar 23 at 7:55
Okay great this makes a lot of sense, thank you for the clarification!
– blewittrb
Mar 23 at 12:34
Okay great this makes a lot of sense, thank you for the clarification!
– blewittrb
Mar 23 at 12:34
add a comment |
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The x-axis in such drawings is usually an arbitrary and conceptual cross-section through a high-dimensional optimisation 'surface'. That's all. It's a picture to illustrate a concept. Yes, as you put it, you are over-thinking it.
– High Performance Mark
Mar 23 at 7:55
Okay great this makes a lot of sense, thank you for the clarification!
– blewittrb
Mar 23 at 12:34