In simple hill climbing, the first closer node is chosen, whereas in steepest ascent hill climbing all successors are compared and the closest to the solution is chosen.
Hill Climbing
The 'generate and test' type of search algorithms presented above only expands the search space and examines the existence of the goal in that space.
hill climbing a form of search in which the path of steepest ascent towards the goal is taken at each step. It is excellent if the domain is well-behaved, but can get stuck on local maxima or mesas.
Hill Climbing and Beam search both have inherent problems and unless special care is taken (and sometimes its not practicle to monitor the search and make sure its working correctly) they may not find a path, even if one exists.
Discrete Space Hill Climbing Algorithm currentNode = startNode; loop do L = NEIGHBORS(currentNode); nextEval = -INF; nextNode = NULL; for all x in L if (EVAL(x) > nextEval) nextNode = x; nextEval = EVAL(x); ...
1 Hill Climbing and Dynamic Programming 127
4.2 The Best-First Search Algorithm 133
4.3 Admissibility, Monotonicity, and Informedness 145
4.4 Using Heuristics in Games 150
4.5 Complexity Issues 157
4.6 Epilogue and References 161
4.
In fact, I'd tried messing with hill climbing (well, it isn't hill climbing in the GA sense) to get the biases a little more robust, but it doesn't seem to work that well and it takes a long time to complete.
traditional search methods (such as hill climbing), genetic programming remains a sort of pariah amongst the various techniques of search.
There are many methods, how to find some suitable solution (ie. not necessarily the best solution), for example hill climbing, tabu search, simulated annealing and genetic algorithm.
Local searches, such as hill climbing, simulated annealing and beam search, use techniques borrowed from optimization theory.[82] ...
 See also: Search algorithm, Dynamic programming, Depth-first search, Heuristics, Artificial intelligence
  
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